CS 422
Sneha Shahaji Kadam
Part 2.1
rm(list=ls())
country_df <- read.csv("countries.csv",header=TRUE,row.names=1)Part 2.1(a)(i)
summary(country_df) GDP HIV Lifeexp
Min. : 3685 Min. :0.1000 Min. :65.90
1st Qu.: 9640 1st Qu.:0.1000 1st Qu.:72.55
Median :27418 Median :0.3000 Median :78.00
Mean :22296 Mean :0.4133 Mean :76.06
3rd Qu.:31832 3rd Qu.:0.6500 3rd Qu.:79.85
Max. :43369 Max. :1.1000 Max. :82.00
Mil Oilcons Pop
Min. :0.500 Min. : 0.80 Min. : 33.0
1st Qu.:1.350 1st Qu.: 5.25 1st Qu.: 59.5
Median :2.500 Median :11.30 Median : 109.0
Mean :2.253 Mean :10.91 Mean : 262.8
3rd Qu.:2.700 3rd Qu.:15.10 3rd Qu.: 212.5
Max. :4.300 Max. :25.10 Max. :1322.0
Tel Unempl
Min. : 44.0 Min. : 2.90
1st Qu.:241.3 1st Qu.: 4.15
Median :432.8 Median : 6.60
Mean :391.3 Mean : 6.42
3rd Qu.:550.4 3rd Qu.: 7.95
Max. :657.8 Max. :12.50 Part 2.1(a)(ii)
boxplot(country_df$GDP,main="Boxplot for GDP")
boxplot(country_df$HIV,main="Boxplot for HIV")
boxplot(country_df$Lifeexp,main="Boxplot for Lifeexp")
boxplot(country_df$Mil,main="Boxplot for Mil")
boxplot(country_df$Oilcons,main="Boxplot for Oilcons")
boxplot(country_df$Pop,main="Boxplot for Pop")
boxplot(country_df$Tel,main="Boxplot for Tel")
boxplot(country_df$Unempl,main="Boxplot for Unempl")
## Boxplot for Pop has 2 outliers.Those 2 observations are numerically distant from the rest of the data.This attribute captures most of the variance.Part 2.1(b)
pca_country_df <- prcomp(country_df,scale.=T)Part 2.1(c)(i)
summary(pca_country_df)Importance of components:
PC1 PC2 PC3 PC4
Standard deviation 2.0605 1.1379 1.0170 0.8690
Proportion of Variance 0.5307 0.1618 0.1293 0.0944
Cumulative Proportion 0.5307 0.6925 0.8218 0.9162
PC5 PC6 PC7 PC8
Standard deviation 0.59319 0.41733 0.33233 0.1833
Proportion of Variance 0.04398 0.02177 0.01381 0.0042
Cumulative Proportion 0.96022 0.98199 0.99580 1.0000#First four components explains atleast 90% of the variance.Part 2.1(c)(ii)
screeplot(pca_country_df,type="lines")
Part 2.1(c)(iii)
#Based on the screeplot, we'll choose first four components to use for modeling to engage in a feature reduction task.Part 2.1(d)
rotation <- pca_country_df$rotation
rotation PC1 PC2 PC3
GDP 0.4560268 0.06362084 -0.271093835
HIV -0.1934368 -0.32800239 -0.694218852
Lifeexp 0.4407804 0.01452102 0.187653505
Mil -0.1964834 0.56706336 -0.480082473
Oilcons 0.4275287 0.03644676 -0.320454603
Pop -0.3150143 0.54189871 0.006639875
Tel 0.4423016 0.14093180 -0.200094388
Unempl -0.2099599 -0.50174771 -0.190006038
PC4 PC5 PC6
GDP -0.05442480 0.14947678 -0.08002969
HIV 0.48404674 0.19548002 -0.23804695
Lifeexp -0.14486052 0.44812657 -0.24828417
Mil -0.41625346 -0.32517176 -0.05002767
Oilcons 0.04650622 0.07839373 0.78889952
Pop 0.07981396 0.72635243 0.05264696
Tel -0.11206139 -0.07267202 -0.49436031
Unempl -0.73986102 0.30639110 0.05705115
PC7 PC8
GDP 0.1466324 0.81319670
HIV 0.1456698 -0.15052276
Lifeexp 0.5683508 -0.40474473
Mil 0.3340639 -0.12747214
Oilcons -0.1184137 -0.26173851
Pop -0.2583224 0.06006144
Tel -0.6471483 -0.25186858
Unempl -0.1520024 0.02084195#rotation <- -rotation
#pca_country_df$x <- -pca_country_df$xPart 2.1(d)(i)
#PC1 is positively correlated to GDP, Lifeexp, Oilcons, Tel attributes.
#PC1 is negatively correlated to HIV, Mil, Pop, Unempl attributes.Part 2.1(d)(ii)
#PC2 is positively correlated to GDP, Lifeexp,Mil, Oilcons,Pop, Tel attributes.
#PC2 is negatively correlated to HIV, Unempl attributes.Part 2.1(e)
biplot(pca_country_df,scale=0)
Part 2.1(e)(i)
pca_country_df$x[c(1,14,9),c(1,2)] PC1 PC2
Brazil -2.037017 -1.03998472
UK 1.456991 0.83445072
Japan 2.013116 -0.05735491Part 2.1(e)(ii)
#PC1 is high for Japan and lowest for Brazil.
#PC1 is positively correlated to GDP, Lifeexp, Oilcons, Tel attributes.
#Japan has higher values in GDP,LifeExp, Oilcons,Tel attributes compare to Brazil and UK.
#PC2 is high for UK and lowest for Brazil.
#PC2 is positively correlated to GDP, Lifeexp,Mil, Oilcons,Pop, Tel attributes.
#UK has higher values in GDP, Lifeexp,Mil, Oilcons,Pop, Tel attributes compare to Japan and Brazil.
#PC1 and PC2 for Brazil, UK, and Japan make sense.Part 2.2
library(tensorflow)
library(keras)
library(dplyr)
library(caret)
rm(list=ls())
df <- read.csv("activity-small.csv")
set.seed(1122)
df <- df[sample(nrow(df)), ]
indx <- sample(1:nrow(df), 0.20*nrow(df))
test.df <- df[indx, ]
train.df <- df[-indx, ]
label.test <- test.df$label
test.df$label <- NULL
test.df <- as.data.frame(scale(test.df))
test.df$label <- label.test
rm(label.test)
label.train <- train.df$label
train.df$label <- NULL
train.df <- as.data.frame(scale(train.df))
train.df$label <- label.train
rm(label.train)
rm(indx)Part 2.2 (a)
X_train <- select(train.df, -label)
y_train <- train.df$label
y_train.ohe <- to_categorical(y_train)
X_test <- select(test.df, -label)
y_test <- test.df$label
y_test.ohe <- to_categorical(test.df$label)
model <- keras_model_sequential() %>%
layer_dense(units = 10, activation="relu", input_shape=c(3)) %>%
layer_dense(units = 4, activation="softmax")
model %>%
compile(loss = "categorical_crossentropy", optimizer="adam", metrics=c("accuracy"))
model %>% fit(
data.matrix(X_train),
y_train.ohe,
epochs=100,
batch_size=1,
validation_split=0.20
)Train on 640 samples, validate on 160 samples
Epoch 1/100
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640/640 [==============================] - 3s 4ms/sample - loss: 1.1839 - accuracy: 0.4500 - val_loss: 1.0066 - val_accuracy: 0.6938
Epoch 2/100
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640/640 [==============================] - 2s 4ms/sample - loss: 0.9198 - accuracy: 0.6656 - val_loss: 0.8156 - val_accuracy: 0.6562
Epoch 3/100
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640/640 [==============================] - 2s 4ms/sample - loss: 0.7778 - accuracy: 0.6703 - val_loss: 0.7342 - val_accuracy: 0.7188
Epoch 4/100
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640/640 [==============================] - 2s 4ms/sample - loss: 0.7137 - accuracy: 0.6828 - val_loss: 0.6916 - val_accuracy: 0.7563
Epoch 5/100
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640/640 [==============================] - 2s 4ms/sample - loss: 0.6785 - accuracy: 0.7250 - val_loss: 0.6622 - val_accuracy: 0.7500
Epoch 6/100
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640/640 [==============================] - 3s 4ms/sample - loss: 0.6517 - accuracy: 0.7609 - val_loss: 0.6473 - val_accuracy: 0.6750
Epoch 7/100
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640/640 [==============================] - 3s 5ms/sample - loss: 0.6360 - accuracy: 0.7453 - val_loss: 0.6320 - val_accuracy: 0.7250
Epoch 8/100
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640/640 [==============================] - 4s 6ms/sample - loss: 0.6207 - accuracy: 0.7547 - val_loss: 0.6191 - val_accuracy: 0.7312
Epoch 9/100
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640/640 [==============================] - 4s 7ms/sample - loss: 0.6105 - accuracy: 0.7656 - val_loss: 0.6108 - val_accuracy: 0.7437
Epoch 10/100
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640/640 [==============================] - 4s 6ms/sample - loss: 0.6018 - accuracy: 0.7734 - val_loss: 0.6024 - val_accuracy: 0.7312
Epoch 11/100
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640/640 [==============================] - 3s 4ms/sample - loss: 0.5938 - accuracy: 0.7766 - val_loss: 0.5980 - val_accuracy: 0.7375
Epoch 12/100
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640/640 [==============================] - 3s 4ms/sample - loss: 0.5877 - accuracy: 0.7719 - val_loss: 0.5905 - val_accuracy: 0.7312
Epoch 13/100
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640/640 [==============================] - 3s 4ms/sample - loss: 0.5809 - accuracy: 0.7750 - val_loss: 0.5884 - val_accuracy: 0.7250
Epoch 14/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5778 - accuracy: 0.7672 - val_loss: 0.5820 - val_accuracy: 0.7312
Epoch 15/100
1/640 [..............................] - ETA: 1s - loss: 1.7529 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5719 - accuracy: 0.7719 - val_loss: 0.5799 - val_accuracy: 0.7250
Epoch 16/100
1/640 [..............................] - ETA: 8s - loss: 0.6616 - accuracy: 1.0000
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5678 - accuracy: 0.7781 - val_loss: 0.5751 - val_accuracy: 0.7188
Epoch 17/100
1/640 [..............................] - ETA: 7s - loss: 0.3418 - accuracy: 1.0000
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5638 - accuracy: 0.7750 - val_loss: 0.5763 - val_accuracy: 0.7250
Epoch 18/100
1/640 [..............................] - ETA: 1s - loss: 0.1158 - accuracy: 1.0000
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5594 - accuracy: 0.7719 - val_loss: 0.5765 - val_accuracy: 0.7250
Epoch 19/100
1/640 [..............................] - ETA: 2s - loss: 1.1752 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5579 - accuracy: 0.7766 - val_loss: 0.5731 - val_accuracy: 0.7250
Epoch 20/100
1/640 [..............................] - ETA: 4s - loss: 0.1770 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5543 - accuracy: 0.7797 - val_loss: 0.5697 - val_accuracy: 0.7188
Epoch 21/100
1/640 [..............................] - ETA: 4s - loss: 0.0465 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5516 - accuracy: 0.7922 - val_loss: 0.5656 - val_accuracy: 0.7188
Epoch 22/100
1/640 [..............................] - ETA: 1s - loss: 0.4652 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5491 - accuracy: 0.7906 - val_loss: 0.5671 - val_accuracy: 0.7188
Epoch 23/100
1/640 [..............................] - ETA: 0s - loss: 1.0238 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5458 - accuracy: 0.7891 - val_loss: 0.5661 - val_accuracy: 0.7188
Epoch 24/100
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5437 - accuracy: 0.7859 - val_loss: 0.5640 - val_accuracy: 0.7188
Epoch 25/100
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5411 - accuracy: 0.7937 - val_loss: 0.5608 - val_accuracy: 0.7188
Epoch 26/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5383 - accuracy: 0.7953 - val_loss: 0.5598 - val_accuracy: 0.7125
Epoch 27/100
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5357 - accuracy: 0.7844 - val_loss: 0.5579 - val_accuracy: 0.7250
Epoch 28/100
1/640 [..............................] - ETA: 3s - loss: 0.3071 - accuracy: 1.0000
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5342 - accuracy: 0.7953 - val_loss: 0.5591 - val_accuracy: 0.7312
Epoch 29/100
1/640 [..............................] - ETA: 3s - loss: 0.2293 - accuracy: 1.0000
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5330 - accuracy: 0.8000 - val_loss: 0.5593 - val_accuracy: 0.7312
Epoch 30/100
1/640 [..............................] - ETA: 2s - loss: 0.4680 - accuracy: 1.0000
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5312 - accuracy: 0.7937 - val_loss: 0.5579 - val_accuracy: 0.7250
Epoch 31/100
1/640 [..............................] - ETA: 3s - loss: 0.8724 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5304 - accuracy: 0.7984 - val_loss: 0.5546 - val_accuracy: 0.7250
Epoch 32/100
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5285 - accuracy: 0.8078 - val_loss: 0.5585 - val_accuracy: 0.7250
Epoch 33/100
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5272 - accuracy: 0.7984 - val_loss: 0.5582 - val_accuracy: 0.7250
Epoch 34/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5259 - accuracy: 0.8000 - val_loss: 0.5551 - val_accuracy: 0.7312
Epoch 35/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5260 - accuracy: 0.7953 - val_loss: 0.5527 - val_accuracy: 0.7312
Epoch 36/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5247 - accuracy: 0.7984 - val_loss: 0.5569 - val_accuracy: 0.7188
Epoch 37/100
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5230 - accuracy: 0.7969 - val_loss: 0.5556 - val_accuracy: 0.7250
Epoch 38/100
1/640 [..............................] - ETA: 5s - loss: 0.4816 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5224 - accuracy: 0.7984 - val_loss: 0.5552 - val_accuracy: 0.7375
Epoch 39/100
1/640 [..............................] - ETA: 2s - loss: 0.0445 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5220 - accuracy: 0.7984 - val_loss: 0.5524 - val_accuracy: 0.7250
Epoch 40/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5197 - accuracy: 0.7969 - val_loss: 0.5535 - val_accuracy: 0.7250
Epoch 41/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5195 - accuracy: 0.7969 - val_loss: 0.5532 - val_accuracy: 0.7375
Epoch 42/100
1/640 [..............................] - ETA: 5s - loss: 0.9731 - accuracy: 0.0000e+00
10/640 [..............................] - ETA: 3s - loss: 1.2522 - accuracy: 0.6000
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5192 - accuracy: 0.8000 - val_loss: 0.5518 - val_accuracy: 0.7250
Epoch 43/100
1/640 [..............................] - ETA: 4s - loss: 0.6128 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5178 - accuracy: 0.7937 - val_loss: 0.5513 - val_accuracy: 0.7312
Epoch 44/100
1/640 [..............................] - ETA: 2s - loss: 0.8846 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5178 - accuracy: 0.8078 - val_loss: 0.5512 - val_accuracy: 0.7250
Epoch 45/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5164 - accuracy: 0.7969 - val_loss: 0.5532 - val_accuracy: 0.7312
Epoch 46/100
1/640 [..............................] - ETA: 2s - loss: 1.1822 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5150 - accuracy: 0.7984 - val_loss: 0.5547 - val_accuracy: 0.7312
Epoch 47/100
1/640 [..............................] - ETA: 4s - loss: 0.0263 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5143 - accuracy: 0.8000 - val_loss: 0.5527 - val_accuracy: 0.7312
Epoch 48/100
1/640 [..............................] - ETA: 4s - loss: 2.6485e-04 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5136 - accuracy: 0.7953 - val_loss: 0.5492 - val_accuracy: 0.7250
Epoch 49/100
1/640 [..............................] - ETA: 2s - loss: 0.0057 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5106 - accuracy: 0.7937 - val_loss: 0.5562 - val_accuracy: 0.7312
Epoch 50/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5117 - accuracy: 0.7969 - val_loss: 0.5531 - val_accuracy: 0.7312
Epoch 51/100
1/640 [..............................] - ETA: 9s - loss: 0.0800 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5128 - accuracy: 0.7969 - val_loss: 0.5489 - val_accuracy: 0.7312
Epoch 52/100
1/640 [..............................] - ETA: 4s - loss: 0.3170 - accuracy: 1.0000
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5102 - accuracy: 0.8094 - val_loss: 0.5556 - val_accuracy: 0.7250
Epoch 53/100
1/640 [..............................] - ETA: 2s - loss: 0.0240 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5116 - accuracy: 0.8000 - val_loss: 0.5572 - val_accuracy: 0.7312
Epoch 54/100
1/640 [..............................] - ETA: 2s - loss: 0.5384 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5091 - accuracy: 0.8016 - val_loss: 0.5536 - val_accuracy: 0.7312
Epoch 55/100
1/640 [..............................] - ETA: 1s - loss: 1.1786 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5104 - accuracy: 0.8031 - val_loss: 0.5516 - val_accuracy: 0.7312
Epoch 56/100
1/640 [..............................] - ETA: 2s - loss: 4.0555 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5085 - accuracy: 0.8047 - val_loss: 0.5501 - val_accuracy: 0.7250
Epoch 57/100
1/640 [..............................] - ETA: 7s - loss: 0.0000e+00 - accuracy: 1.0000
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5074 - accuracy: 0.7984 - val_loss: 0.5503 - val_accuracy: 0.7250
Epoch 58/100
1/640 [..............................] - ETA: 3s - loss: 0.2244 - accuracy: 1.0000
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5071 - accuracy: 0.8047 - val_loss: 0.5496 - val_accuracy: 0.7312
Epoch 59/100
1/640 [..............................] - ETA: 2s - loss: 0.0000e+00 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5069 - accuracy: 0.8062 - val_loss: 0.5505 - val_accuracy: 0.7312
Epoch 60/100
1/640 [..............................] - ETA: 1s - loss: 8.4376e-04 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5045 - accuracy: 0.8047 - val_loss: 0.5522 - val_accuracy: 0.7312
Epoch 61/100
1/640 [..............................] - ETA: 4s - loss: 0.0696 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5042 - accuracy: 0.8047 - val_loss: 0.5533 - val_accuracy: 0.7312
Epoch 62/100
1/640 [..............................] - ETA: 6s - loss: 0.0366 - accuracy: 1.0000
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640/640 [==============================] - 3s 4ms/sample - loss: 0.5033 - accuracy: 0.8094 - val_loss: 0.5562 - val_accuracy: 0.7188
Epoch 63/100
1/640 [..............................] - ETA: 1s - loss: 0.4701 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5032 - accuracy: 0.8109 - val_loss: 0.5545 - val_accuracy: 0.7375
Epoch 64/100
1/640 [..............................] - ETA: 5s - loss: 0.3655 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5032 - accuracy: 0.8078 - val_loss: 0.5483 - val_accuracy: 0.7188
Epoch 65/100
1/640 [..............................] - ETA: 6s - loss: 0.9316 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5020 - accuracy: 0.8031 - val_loss: 0.5486 - val_accuracy: 0.7375
Epoch 66/100
1/640 [..............................] - ETA: 7s - loss: 0.1786 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5024 - accuracy: 0.8109 - val_loss: 0.5526 - val_accuracy: 0.7437
Epoch 67/100
1/640 [..............................] - ETA: 1s - loss: 0.0527 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5023 - accuracy: 0.8078 - val_loss: 0.5527 - val_accuracy: 0.7375
Epoch 68/100
1/640 [..............................] - ETA: 5s - loss: 0.0077 - accuracy: 1.0000
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640/640 [==============================] - 3s 4ms/sample - loss: 0.5007 - accuracy: 0.8062 - val_loss: 0.5505 - val_accuracy: 0.7437
Epoch 69/100
1/640 [..............................] - ETA: 5s - loss: 0.3417 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4998 - accuracy: 0.8031 - val_loss: 0.5534 - val_accuracy: 0.7250
Epoch 70/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4995 - accuracy: 0.8094 - val_loss: 0.5505 - val_accuracy: 0.7188
Epoch 71/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4983 - accuracy: 0.8062 - val_loss: 0.5531 - val_accuracy: 0.7375
Epoch 72/100
1/640 [..............................] - ETA: 7s - loss: 0.0228 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4996 - accuracy: 0.8062 - val_loss: 0.5509 - val_accuracy: 0.7375
Epoch 73/100
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640/640 [==============================] - 2s 4ms/sample - loss: 0.4984 - accuracy: 0.8141 - val_loss: 0.5511 - val_accuracy: 0.7250
Epoch 74/100
1/640 [..............................] - ETA: 12s - loss: 0.9948 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4964 - accuracy: 0.8062 - val_loss: 0.5541 - val_accuracy: 0.7437
Epoch 75/100
1/640 [..............................] - ETA: 2s - loss: 0.0296 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4983 - accuracy: 0.8062 - val_loss: 0.5520 - val_accuracy: 0.7312
Epoch 76/100
1/640 [..............................] - ETA: 1s - loss: 1.2372 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4973 - accuracy: 0.8109 - val_loss: 0.5509 - val_accuracy: 0.7250
Epoch 77/100
1/640 [..............................] - ETA: 0s - loss: 1.5115e-04 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4958 - accuracy: 0.8047 - val_loss: 0.5497 - val_accuracy: 0.7312
Epoch 78/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4962 - accuracy: 0.8047 - val_loss: 0.5496 - val_accuracy: 0.7437
Epoch 79/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4959 - accuracy: 0.8062 - val_loss: 0.5526 - val_accuracy: 0.7437
Epoch 80/100
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640/640 [==============================] - 2s 4ms/sample - loss: 0.4942 - accuracy: 0.8094 - val_loss: 0.5469 - val_accuracy: 0.7312
Epoch 81/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4954 - accuracy: 0.8141 - val_loss: 0.5471 - val_accuracy: 0.7437
Epoch 82/100
1/640 [..............................] - ETA: 2s - loss: 1.9796 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4952 - accuracy: 0.8109 - val_loss: 0.5471 - val_accuracy: 0.7250
Epoch 83/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4945 - accuracy: 0.8094 - val_loss: 0.5452 - val_accuracy: 0.7250
Epoch 84/100
1/640 [..............................] - ETA: 5s - loss: 0.6465 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4930 - accuracy: 0.8062 - val_loss: 0.5521 - val_accuracy: 0.7312
Epoch 85/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4934 - accuracy: 0.8141 - val_loss: 0.5480 - val_accuracy: 0.7250
Epoch 86/100
1/640 [..............................] - ETA: 3s - loss: 1.0829 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4919 - accuracy: 0.8078 - val_loss: 0.5447 - val_accuracy: 0.7437
Epoch 87/100
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640/640 [==============================] - 2s 4ms/sample - loss: 0.4940 - accuracy: 0.8141 - val_loss: 0.5497 - val_accuracy: 0.7312
Epoch 88/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4930 - accuracy: 0.8078 - val_loss: 0.5483 - val_accuracy: 0.7375
Epoch 89/100
1/640 [..............................] - ETA: 3s - loss: 0.2310 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4935 - accuracy: 0.8078 - val_loss: 0.5465 - val_accuracy: 0.7375
Epoch 90/100
1/640 [..............................] - ETA: 4s - loss: 0.6704 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4919 - accuracy: 0.8062 - val_loss: 0.5472 - val_accuracy: 0.7375
Epoch 91/100
1/640 [..............................] - ETA: 5s - loss: 0.1666 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4914 - accuracy: 0.8016 - val_loss: 0.5457 - val_accuracy: 0.7250
Epoch 92/100
1/640 [..............................] - ETA: 2s - loss: 0.0374 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4906 - accuracy: 0.8078 - val_loss: 0.5456 - val_accuracy: 0.7375
Epoch 93/100
1/640 [..............................] - ETA: 1s - loss: 0.7992 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4914 - accuracy: 0.8062 - val_loss: 0.5462 - val_accuracy: 0.7375
Epoch 94/100
1/640 [..............................] - ETA: 2s - loss: 0.0297 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4905 - accuracy: 0.8125 - val_loss: 0.5489 - val_accuracy: 0.7250
Epoch 95/100
1/640 [..............................] - ETA: 3s - loss: 1.4424 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4902 - accuracy: 0.8109 - val_loss: 0.5468 - val_accuracy: 0.7312
Epoch 96/100
1/640 [..............................] - ETA: 6s - loss: 0.3247 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4896 - accuracy: 0.8094 - val_loss: 0.5460 - val_accuracy: 0.7500
Epoch 97/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4905 - accuracy: 0.8078 - val_loss: 0.5479 - val_accuracy: 0.7437
Epoch 98/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4884 - accuracy: 0.8125 - val_loss: 0.5468 - val_accuracy: 0.7437
Epoch 99/100
1/640 [..............................] - ETA: 1s - loss: 0.4586 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4896 - accuracy: 0.8078 - val_loss: 0.5443 - val_accuracy: 0.7500
Epoch 100/100
1/640 [..............................] - ETA: 1s - loss: 0.0383 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4879 - accuracy: 0.8094 - val_loss: 0.5462 - val_accuracy: 0.7375model %>% evaluate(as.matrix(X_test), y_test.ohe)
200/1 [======================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================== 0.7284901
$accuracy
[1] 0.815pred.class <- model %>% predict_classes(as.matrix(X_test))
pred.prob <- model %>% predict(as.matrix(X_test)) %>% round(3)
conf <- confusionMatrix(as.factor(y_test), as.factor(pred.class))
#(i)Overall Accuracy
cat(paste0("Overall Accuracy"))Overall Accuracyprint(conf$overall[1])Accuracy
0.815 #(ii)Per-class sensitivity, specificity, and balanced accuracy on the test dataset
cat(paste0("Per-class sensitivity"))Per-class sensitivityprint(conf$byClass[,1]) Class: 0 Class: 1 Class: 2 Class: 3
0.9166667 0.7333333 0.7755102 0.8387097 cat(paste0("Per-class Specificity"))Per-class Specificityprint(conf$byClass[,2]) Class: 0 Class: 1 Class: 2 Class: 3
0.9857143 0.9357143 0.9735099 0.8698225 cat(paste0("Per-class Balance accuracy"))Per-class Balance accuracyprint(conf$byClass[,11]) Class: 0 Class: 1 Class: 2 Class: 3
0.9511905 0.8345238 0.8745101 0.8542661 part 2.2(b)
rm(model)
batch <- c(1,32,64,128,256)
i <- 1
for (i in 1:5)
{
model <- keras_model_sequential() %>%
layer_dense(units = 10, activation="relu", input_shape=c(3)) %>%
layer_dense(units = 4, activation="softmax")
model %>%
compile(loss = "categorical_crossentropy", optimizer="adam", metrics=c("accuracy"))
cat(paste0("neural network for batch size: ",batch[i]))
cat(paste0("\n"))
begin <- Sys.time()
model %>% fit(
data.matrix(X_train),
y_train.ohe,
epochs=100,
batch_size=batch[i],
validation_split=0.20
)
end <- Sys.time()
time_elapsed <- end - begin
cat(paste0("Time taken to train model: ",time_elapsed))
i=i+1
model %>% evaluate(as.matrix(X_test), y_test.ohe)
pred.class <- model %>% predict_classes(as.matrix(X_test))
pred.prob <- model %>% predict(as.matrix(X_test)) %>% round(3)
conf <- confusionMatrix(as.factor(y_test), as.factor(pred.class))
print(conf)
rm(model)
}neural network for batch size: 1
Train on 640 samples, validate on 160 samples
Epoch 1/100
1/640 [..............................] - ETA: 4:06 - loss: 1.8153 - accuracy: 0.0000e+00
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640/640 [==============================] - 3s 4ms/sample - loss: 1.2876 - accuracy: 0.3922 - val_loss: 1.0999 - val_accuracy: 0.5250
Epoch 2/100
1/640 [..............................] - ETA: 4s - loss: 0.8325 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.9807 - accuracy: 0.5562 - val_loss: 0.8828 - val_accuracy: 0.6562
Epoch 3/100
1/640 [..............................] - ETA: 7s - loss: 0.0512 - accuracy: 1.0000
8/640 [..............................] - ETA: 5s - loss: 0.5846 - accuracy: 0.8750
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640/640 [==============================] - 2s 4ms/sample - loss: 0.8226 - accuracy: 0.6391 - val_loss: 0.7836 - val_accuracy: 0.6938
Epoch 4/100
1/640 [..............................] - ETA: 4s - loss: 1.0071 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 3ms/sample - loss: 0.7556 - accuracy: 0.6719 - val_loss: 0.7365 - val_accuracy: 0.7000
Epoch 5/100
1/640 [..............................] - ETA: 2s - loss: 0.1389 - accuracy: 1.0000
15/640 [..............................] - ETA: 2s - loss: 0.8535 - accuracy: 0.5333
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640/640 [==============================] - 2s 3ms/sample - loss: 0.7173 - accuracy: 0.6719 - val_loss: 0.7083 - val_accuracy: 0.6875
Epoch 6/100
1/640 [..............................] - ETA: 1s - loss: 0.8583 - accuracy: 0.0000e+00
28/640 [>.............................] - ETA: 1s - loss: 0.7966 - accuracy: 0.5714
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640/640 [==============================] - 2s 4ms/sample - loss: 0.6933 - accuracy: 0.6781 - val_loss: 0.6897 - val_accuracy: 0.6687
Epoch 7/100
1/640 [..............................] - ETA: 0s - loss: 0.7573 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.6741 - accuracy: 0.6797 - val_loss: 0.6762 - val_accuracy: 0.7563
Epoch 8/100
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640/640 [==============================] - 2s 4ms/sample - loss: 0.6601 - accuracy: 0.7281 - val_loss: 0.6624 - val_accuracy: 0.6625
Epoch 9/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.6494 - accuracy: 0.7203 - val_loss: 0.6537 - val_accuracy: 0.7500
Epoch 10/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.6400 - accuracy: 0.7312 - val_loss: 0.6476 - val_accuracy: 0.7750
Epoch 11/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.6306 - accuracy: 0.7344 - val_loss: 0.6386 - val_accuracy: 0.7750
Epoch 12/100
1/640 [..............................] - ETA: 3s - loss: 0.8240 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 3ms/sample - loss: 0.6224 - accuracy: 0.7516 - val_loss: 0.6367 - val_accuracy: 0.7563
Epoch 13/100
1/640 [..............................] - ETA: 4s - loss: 0.7399 - accuracy: 1.0000
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640/640 [==============================] - 2s 4ms/sample - loss: 0.6145 - accuracy: 0.7625 - val_loss: 0.6282 - val_accuracy: 0.7312
Epoch 14/100
1/640 [..............................] - ETA: 5s - loss: 0.0775 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.6090 - accuracy: 0.7563 - val_loss: 0.6296 - val_accuracy: 0.7188
Epoch 15/100
1/640 [..............................] - ETA: 2s - loss: 0.1636 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.6039 - accuracy: 0.7688 - val_loss: 0.6234 - val_accuracy: 0.7125
Epoch 16/100
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5960 - accuracy: 0.7531 - val_loss: 0.6204 - val_accuracy: 0.7125
Epoch 17/100
1/640 [..............................] - ETA: 1s - loss: 0.6711 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5921 - accuracy: 0.7469 - val_loss: 0.6152 - val_accuracy: 0.7375
Epoch 18/100
1/640 [..............................] - ETA: 6s - loss: 0.4909 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5865 - accuracy: 0.7766 - val_loss: 0.6110 - val_accuracy: 0.7125
Epoch 19/100
1/640 [..............................] - ETA: 4s - loss: 0.0443 - accuracy: 1.0000
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5837 - accuracy: 0.7703 - val_loss: 0.6099 - val_accuracy: 0.7188
Epoch 20/100
1/640 [..............................] - ETA: 2s - loss: 0.6538 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5793 - accuracy: 0.7734 - val_loss: 0.6069 - val_accuracy: 0.7188
Epoch 21/100
1/640 [..............................] - ETA: 1s - loss: 0.0085 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5783 - accuracy: 0.7844 - val_loss: 0.6065 - val_accuracy: 0.7188
Epoch 22/100
1/640 [..............................] - ETA: 5s - loss: 0.6495 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5743 - accuracy: 0.7812 - val_loss: 0.6040 - val_accuracy: 0.7125
Epoch 23/100
1/640 [..............................] - ETA: 2s - loss: 0.0273 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5728 - accuracy: 0.7781 - val_loss: 0.6016 - val_accuracy: 0.7188
Epoch 24/100
1/640 [..............................] - ETA: 8s - loss: 0.0784 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5711 - accuracy: 0.7906 - val_loss: 0.5998 - val_accuracy: 0.7188
Epoch 25/100
1/640 [..............................] - ETA: 2s - loss: 0.0036 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5665 - accuracy: 0.7906 - val_loss: 0.6010 - val_accuracy: 0.7063
Epoch 26/100
1/640 [..............................] - ETA: 0s - loss: 1.7962 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5658 - accuracy: 0.7766 - val_loss: 0.6003 - val_accuracy: 0.7000
Epoch 27/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5624 - accuracy: 0.7828 - val_loss: 0.5972 - val_accuracy: 0.7000
Epoch 28/100
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5619 - accuracy: 0.7906 - val_loss: 0.5970 - val_accuracy: 0.7000
Epoch 29/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5572 - accuracy: 0.7922 - val_loss: 0.5999 - val_accuracy: 0.6938
Epoch 30/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5571 - accuracy: 0.7859 - val_loss: 0.5950 - val_accuracy: 0.7063
Epoch 31/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5543 - accuracy: 0.7844 - val_loss: 0.5929 - val_accuracy: 0.7063
Epoch 32/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5531 - accuracy: 0.7953 - val_loss: 0.5931 - val_accuracy: 0.7188
Epoch 33/100
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5520 - accuracy: 0.7922 - val_loss: 0.5937 - val_accuracy: 0.6875
Epoch 34/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5507 - accuracy: 0.7875 - val_loss: 0.5949 - val_accuracy: 0.6938
Epoch 35/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5484 - accuracy: 0.7859 - val_loss: 0.5922 - val_accuracy: 0.7063
Epoch 36/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5456 - accuracy: 0.7937 - val_loss: 0.5924 - val_accuracy: 0.6875
Epoch 37/100
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5440 - accuracy: 0.7906 - val_loss: 0.5892 - val_accuracy: 0.7063
Epoch 38/100
1/640 [..............................] - ETA: 6s - loss: 0.0854 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5427 - accuracy: 0.7906 - val_loss: 0.5886 - val_accuracy: 0.7125
Epoch 39/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5416 - accuracy: 0.7906 - val_loss: 0.5879 - val_accuracy: 0.7125
Epoch 40/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5387 - accuracy: 0.7984 - val_loss: 0.5870 - val_accuracy: 0.7063
Epoch 41/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5381 - accuracy: 0.7922 - val_loss: 0.5871 - val_accuracy: 0.7188
Epoch 42/100
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5377 - accuracy: 0.7937 - val_loss: 0.5837 - val_accuracy: 0.7125
Epoch 43/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5343 - accuracy: 0.7969 - val_loss: 0.5837 - val_accuracy: 0.7125
Epoch 44/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5354 - accuracy: 0.7969 - val_loss: 0.5823 - val_accuracy: 0.7125
Epoch 45/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5325 - accuracy: 0.7875 - val_loss: 0.5818 - val_accuracy: 0.7188
Epoch 46/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5324 - accuracy: 0.7937 - val_loss: 0.5783 - val_accuracy: 0.7188
Epoch 47/100
1/640 [..............................] - ETA: 2s - loss: 1.0678 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5304 - accuracy: 0.7969 - val_loss: 0.5798 - val_accuracy: 0.7188
Epoch 48/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5302 - accuracy: 0.7953 - val_loss: 0.5796 - val_accuracy: 0.7063
Epoch 49/100
1/640 [..............................] - ETA: 9s - loss: 0.0098 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5300 - accuracy: 0.7969 - val_loss: 0.5783 - val_accuracy: 0.7063
Epoch 50/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5272 - accuracy: 0.7906 - val_loss: 0.5806 - val_accuracy: 0.7188
Epoch 51/100
1/640 [..............................] - ETA: 2s - loss: 0.0550 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5271 - accuracy: 0.7984 - val_loss: 0.5807 - val_accuracy: 0.7063
Epoch 52/100
1/640 [..............................] - ETA: 0s - loss: 0.0478 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5260 - accuracy: 0.7937 - val_loss: 0.5773 - val_accuracy: 0.7063
Epoch 53/100
1/640 [..............................] - ETA: 2s - loss: 0.0481 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5260 - accuracy: 0.8031 - val_loss: 0.5799 - val_accuracy: 0.7063
Epoch 54/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5233 - accuracy: 0.7875 - val_loss: 0.5761 - val_accuracy: 0.7188
Epoch 55/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5246 - accuracy: 0.7953 - val_loss: 0.5817 - val_accuracy: 0.7125
Epoch 56/100
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5234 - accuracy: 0.7891 - val_loss: 0.5756 - val_accuracy: 0.7188
Epoch 57/100
1/640 [..............................] - ETA: 3s - loss: 1.0061 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5210 - accuracy: 0.8000 - val_loss: 0.5781 - val_accuracy: 0.7125
Epoch 58/100
1/640 [..............................] - ETA: 1s - loss: 0.0020 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5229 - accuracy: 0.7969 - val_loss: 0.5773 - val_accuracy: 0.7125
Epoch 59/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5203 - accuracy: 0.8016 - val_loss: 0.5746 - val_accuracy: 0.7188
Epoch 60/100
1/640 [..............................] - ETA: 1s - loss: 8.1059e-05 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5207 - accuracy: 0.7953 - val_loss: 0.5727 - val_accuracy: 0.7063
Epoch 61/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5192 - accuracy: 0.7969 - val_loss: 0.5725 - val_accuracy: 0.7188
Epoch 62/100
1/640 [..............................] - ETA: 5s - loss: 0.3605 - accuracy: 1.0000
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5206 - accuracy: 0.8031 - val_loss: 0.5715 - val_accuracy: 0.7125
Epoch 63/100
1/640 [..............................] - ETA: 9s - loss: 0.1899 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5176 - accuracy: 0.7969 - val_loss: 0.5678 - val_accuracy: 0.7188
Epoch 64/100
1/640 [..............................] - ETA: 1s - loss: 0.3214 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5171 - accuracy: 0.7984 - val_loss: 0.5712 - val_accuracy: 0.7188
Epoch 65/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5166 - accuracy: 0.8016 - val_loss: 0.5711 - val_accuracy: 0.7188
Epoch 66/100
1/640 [..............................] - ETA: 1s - loss: 0.0841 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5138 - accuracy: 0.8031 - val_loss: 0.5680 - val_accuracy: 0.7188
Epoch 67/100
1/640 [..............................] - ETA: 1s - loss: 0.0085 - accuracy: 1.0000
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640/640 [==============================] - 2s 4ms/sample - loss: 0.5134 - accuracy: 0.8047 - val_loss: 0.5678 - val_accuracy: 0.7250
Epoch 68/100
1/640 [..............................] - ETA: 5s - loss: 0.4931 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5118 - accuracy: 0.8000 - val_loss: 0.5646 - val_accuracy: 0.7250
Epoch 69/100
1/640 [..............................] - ETA: 3s - loss: 0.0017 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5131 - accuracy: 0.7969 - val_loss: 0.5645 - val_accuracy: 0.7250
Epoch 70/100
1/640 [..............................] - ETA: 2s - loss: 0.2755 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5109 - accuracy: 0.8031 - val_loss: 0.5632 - val_accuracy: 0.7188
Epoch 71/100
1/640 [..............................] - ETA: 2s - loss: 1.1670e-04 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5105 - accuracy: 0.8062 - val_loss: 0.5639 - val_accuracy: 0.7312
Epoch 72/100
1/640 [..............................] - ETA: 1s - loss: 0.4751 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5095 - accuracy: 0.8016 - val_loss: 0.5616 - val_accuracy: 0.7375
Epoch 73/100
1/640 [..............................] - ETA: 1s - loss: 0.4524 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5099 - accuracy: 0.8000 - val_loss: 0.5639 - val_accuracy: 0.7375
Epoch 74/100
1/640 [..............................] - ETA: 6s - loss: 8.5996e-04 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5074 - accuracy: 0.8047 - val_loss: 0.5665 - val_accuracy: 0.7312
Epoch 75/100
1/640 [..............................] - ETA: 4s - loss: 0.0027 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5067 - accuracy: 0.7906 - val_loss: 0.5630 - val_accuracy: 0.7250
Epoch 76/100
1/640 [..............................] - ETA: 5s - loss: 4.7684e-07 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5050 - accuracy: 0.8078 - val_loss: 0.5637 - val_accuracy: 0.7250
Epoch 77/100
1/640 [..............................] - ETA: 1s - loss: 4.4174 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5049 - accuracy: 0.8000 - val_loss: 0.5638 - val_accuracy: 0.7250
Epoch 78/100
1/640 [..............................] - ETA: 2s - loss: 1.7194 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5054 - accuracy: 0.8109 - val_loss: 0.5634 - val_accuracy: 0.7375
Epoch 79/100
1/640 [..............................] - ETA: 2s - loss: 6.0492e-04 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5051 - accuracy: 0.8047 - val_loss: 0.5611 - val_accuracy: 0.7375
Epoch 80/100
1/640 [..............................] - ETA: 2s - loss: 0.2092 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5032 - accuracy: 0.8047 - val_loss: 0.5623 - val_accuracy: 0.7312
Epoch 81/100
1/640 [..............................] - ETA: 5s - loss: 0.9063 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5030 - accuracy: 0.8078 - val_loss: 0.5691 - val_accuracy: 0.7312
Epoch 82/100
1/640 [..............................] - ETA: 6s - loss: 4.8262 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5032 - accuracy: 0.8047 - val_loss: 0.5625 - val_accuracy: 0.7312
Epoch 83/100
1/640 [..............................] - ETA: 3s - loss: 0.3913 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5019 - accuracy: 0.8062 - val_loss: 0.5634 - val_accuracy: 0.7312
Epoch 84/100
1/640 [..............................] - ETA: 6s - loss: 0.2015 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5013 - accuracy: 0.8031 - val_loss: 0.5644 - val_accuracy: 0.7312
Epoch 85/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5001 - accuracy: 0.8125 - val_loss: 0.5653 - val_accuracy: 0.7312
Epoch 86/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4988 - accuracy: 0.8031 - val_loss: 0.5630 - val_accuracy: 0.7375
Epoch 87/100
1/640 [..............................] - ETA: 2s - loss: 0.0000e+00 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4987 - accuracy: 0.8062 - val_loss: 0.5580 - val_accuracy: 0.7312
Epoch 88/100
1/640 [..............................] - ETA: 2s - loss: 0.0749 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4974 - accuracy: 0.8141 - val_loss: 0.5599 - val_accuracy: 0.7312
Epoch 89/100
1/640 [..............................] - ETA: 4s - loss: 0.0379 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4982 - accuracy: 0.8062 - val_loss: 0.5652 - val_accuracy: 0.7312
Epoch 90/100
1/640 [..............................] - ETA: 5s - loss: 0.0000e+00 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4955 - accuracy: 0.8062 - val_loss: 0.5620 - val_accuracy: 0.7375
Epoch 91/100
1/640 [..............................] - ETA: 5s - loss: 0.0199 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4969 - accuracy: 0.8047 - val_loss: 0.5583 - val_accuracy: 0.7437
Epoch 92/100
1/640 [..............................] - ETA: 1s - loss: 0.3616 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4932 - accuracy: 0.8156 - val_loss: 0.5661 - val_accuracy: 0.7188
Epoch 93/100
1/640 [..............................] - ETA: 1s - loss: 0.6803 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4949 - accuracy: 0.8094 - val_loss: 0.5588 - val_accuracy: 0.7437
Epoch 94/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4951 - accuracy: 0.8078 - val_loss: 0.5594 - val_accuracy: 0.7375
Epoch 95/100
1/640 [..............................] - ETA: 3s - loss: 0.0379 - accuracy: 1.0000
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50/640 [=>............................] - ETA: 1s - loss: 0.5021 - accuracy: 0.7800
75/640 [==>...........................] - ETA: 1s - loss: 0.5484 - accuracy: 0.7733
97/640 [===>..........................] - ETA: 1s - loss: 0.5083 - accuracy: 0.7835
119/640 [====>.........................] - ETA: 1s - loss: 0.5079 - accuracy: 0.7647
145/640 [=====>........................] - ETA: 1s - loss: 0.4863 - accuracy: 0.7655
172/640 [=======>......................] - ETA: 0s - loss: 0.5084 - accuracy: 0.7616
198/640 [========>.....................] - ETA: 0s - loss: 0.5194 - accuracy: 0.7778
227/640 [=========>....................] - ETA: 0s - loss: 0.5269 - accuracy: 0.7753
257/640 [===========>..................] - ETA: 0s - loss: 0.5196 - accuracy: 0.7821
281/640 [============>.................] - ETA: 0s - loss: 0.4989 - accuracy: 0.7972
304/640 [=============>................] - ETA: 0s - loss: 0.5146 - accuracy: 0.7895
324/640 [==============>...............] - ETA: 0s - loss: 0.5110 - accuracy: 0.7901
351/640 [===============>..............] - ETA: 0s - loss: 0.4947 - accuracy: 0.8006
379/640 [================>.............] - ETA: 0s - loss: 0.4936 - accuracy: 0.8021
407/640 [==================>...........] - ETA: 0s - loss: 0.4866 - accuracy: 0.8034
436/640 [===================>..........] - ETA: 0s - loss: 0.4885 - accuracy: 0.8073
465/640 [====================>.........] - ETA: 0s - loss: 0.4780 - accuracy: 0.8151
495/640 [======================>.......] - ETA: 0s - loss: 0.4698 - accuracy: 0.8162
526/640 [=======================>......] - ETA: 0s - loss: 0.4652 - accuracy: 0.8194
556/640 [=========================>....] - ETA: 0s - loss: 0.4726 - accuracy: 0.8237
585/640 [==========================>...] - ETA: 0s - loss: 0.4864 - accuracy: 0.8171
613/640 [===========================>..] - ETA: 0s - loss: 0.4866 - accuracy: 0.8157
640/640 [==============================] - 2s 3ms/sample - loss: 0.4931 - accuracy: 0.8109 - val_loss: 0.5582 - val_accuracy: 0.7437
Epoch 96/100
1/640 [..............................] - ETA: 1s - loss: 0.7183 - accuracy: 1.0000
28/640 [>.............................] - ETA: 1s - loss: 0.6420 - accuracy: 0.7500
55/640 [=>............................] - ETA: 1s - loss: 0.5466 - accuracy: 0.7455
80/640 [==>...........................] - ETA: 1s - loss: 0.5382 - accuracy: 0.7375
103/640 [===>..........................] - ETA: 1s - loss: 0.5906 - accuracy: 0.7087
123/640 [====>.........................] - ETA: 1s - loss: 0.5360 - accuracy: 0.7480
151/640 [======>.......................] - ETA: 1s - loss: 0.5127 - accuracy: 0.7483
178/640 [=======>......................] - ETA: 0s - loss: 0.5137 - accuracy: 0.7640
205/640 [========>.....................] - ETA: 0s - loss: 0.4920 - accuracy: 0.7805
235/640 [==========>...................] - ETA: 0s - loss: 0.4776 - accuracy: 0.7872
260/640 [===========>..................] - ETA: 0s - loss: 0.4685 - accuracy: 0.7885
290/640 [============>.................] - ETA: 0s - loss: 0.4611 - accuracy: 0.7931
318/640 [=============>................] - ETA: 0s - loss: 0.4882 - accuracy: 0.7893
346/640 [===============>..............] - ETA: 0s - loss: 0.4936 - accuracy: 0.7890
368/640 [================>.............] - ETA: 0s - loss: 0.5087 - accuracy: 0.7880
396/640 [=================>............] - ETA: 0s - loss: 0.5017 - accuracy: 0.7904
423/640 [==================>...........] - ETA: 0s - loss: 0.4895 - accuracy: 0.7991
452/640 [====================>.........] - ETA: 0s - loss: 0.4863 - accuracy: 0.8075
478/640 [=====================>........] - ETA: 0s - loss: 0.4851 - accuracy: 0.8117
506/640 [======================>.......] - ETA: 0s - loss: 0.5002 - accuracy: 0.8063
536/640 [========================>.....] - ETA: 0s - loss: 0.4881 - accuracy: 0.8097
564/640 [=========================>....] - ETA: 0s - loss: 0.4834 - accuracy: 0.8121
590/640 [==========================>...] - ETA: 0s - loss: 0.5022 - accuracy: 0.8085
618/640 [===========================>..] - ETA: 0s - loss: 0.4953 - accuracy: 0.8123
640/640 [==============================] - 2s 3ms/sample - loss: 0.4904 - accuracy: 0.8125 - val_loss: 0.5577 - val_accuracy: 0.7437
Epoch 97/100
1/640 [..............................] - ETA: 6s - loss: 0.3529 - accuracy: 1.0000
19/640 [..............................] - ETA: 1s - loss: 0.3349 - accuracy: 0.8947
49/640 [=>............................] - ETA: 1s - loss: 0.4094 - accuracy: 0.8367
71/640 [==>...........................] - ETA: 1s - loss: 0.4063 - accuracy: 0.8169
97/640 [===>..........................] - ETA: 1s - loss: 0.4074 - accuracy: 0.8351
116/640 [====>.........................] - ETA: 1s - loss: 0.4129 - accuracy: 0.8362
141/640 [=====>........................] - ETA: 1s - loss: 0.5238 - accuracy: 0.8227
170/640 [======>.......................] - ETA: 1s - loss: 0.5346 - accuracy: 0.8118
199/640 [========>.....................] - ETA: 0s - loss: 0.5124 - accuracy: 0.8191
226/640 [=========>....................] - ETA: 0s - loss: 0.5084 - accuracy: 0.8186
255/640 [==========>...................] - ETA: 0s - loss: 0.4864 - accuracy: 0.8314
282/640 [============>.................] - ETA: 0s - loss: 0.4899 - accuracy: 0.8191
310/640 [=============>................] - ETA: 0s - loss: 0.4829 - accuracy: 0.8097
339/640 [==============>...............] - ETA: 0s - loss: 0.5001 - accuracy: 0.7906
356/640 [===============>..............] - ETA: 0s - loss: 0.5010 - accuracy: 0.7949
383/640 [================>.............] - ETA: 0s - loss: 0.4987 - accuracy: 0.7911
412/640 [==================>...........] - ETA: 0s - loss: 0.5047 - accuracy: 0.7937
441/640 [===================>..........] - ETA: 0s - loss: 0.4895 - accuracy: 0.8050
471/640 [=====================>........] - ETA: 0s - loss: 0.4730 - accuracy: 0.8153
501/640 [======================>.......] - ETA: 0s - loss: 0.4824 - accuracy: 0.8144
530/640 [=======================>......] - ETA: 0s - loss: 0.4861 - accuracy: 0.8170
557/640 [=========================>....] - ETA: 0s - loss: 0.4905 - accuracy: 0.8151
584/640 [==========================>...] - ETA: 0s - loss: 0.4889 - accuracy: 0.8151
611/640 [===========================>..] - ETA: 0s - loss: 0.4866 - accuracy: 0.8151
640/640 [==============================] - 2s 3ms/sample - loss: 0.4909 - accuracy: 0.8109 - val_loss: 0.5581 - val_accuracy: 0.7312
Epoch 98/100
1/640 [..............................] - ETA: 4s - loss: 0.0249 - accuracy: 1.0000
22/640 [>.............................] - ETA: 1s - loss: 0.6765 - accuracy: 0.6818
52/640 [=>............................] - ETA: 1s - loss: 0.6485 - accuracy: 0.7500
75/640 [==>...........................] - ETA: 1s - loss: 0.5625 - accuracy: 0.8000
100/640 [===>..........................] - ETA: 1s - loss: 0.5064 - accuracy: 0.8200
121/640 [====>.........................] - ETA: 1s - loss: 0.5465 - accuracy: 0.7934
150/640 [======>.......................] - ETA: 1s - loss: 0.4929 - accuracy: 0.8200
172/640 [=======>......................] - ETA: 0s - loss: 0.4904 - accuracy: 0.8314
203/640 [========>.....................] - ETA: 0s - loss: 0.4812 - accuracy: 0.8227
232/640 [=========>....................] - ETA: 0s - loss: 0.4629 - accuracy: 0.8319
261/640 [===========>..................] - ETA: 0s - loss: 0.4715 - accuracy: 0.8276
292/640 [============>.................] - ETA: 0s - loss: 0.4884 - accuracy: 0.8288
322/640 [==============>...............] - ETA: 0s - loss: 0.4899 - accuracy: 0.8261
333/640 [==============>...............] - ETA: 0s - loss: 0.4910 - accuracy: 0.8258
356/640 [===============>..............] - ETA: 0s - loss: 0.4997 - accuracy: 0.8174
386/640 [=================>............] - ETA: 0s - loss: 0.5089 - accuracy: 0.8135
415/640 [==================>...........] - ETA: 0s - loss: 0.5215 - accuracy: 0.8072
442/640 [===================>..........] - ETA: 0s - loss: 0.5161 - accuracy: 0.8054
473/640 [=====================>........] - ETA: 0s - loss: 0.5186 - accuracy: 0.8034
502/640 [======================>.......] - ETA: 0s - loss: 0.5076 - accuracy: 0.8088
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592/640 [==========================>...] - ETA: 0s - loss: 0.4999 - accuracy: 0.8041
623/640 [============================>.] - ETA: 0s - loss: 0.4932 - accuracy: 0.8090
640/640 [==============================] - 2s 3ms/sample - loss: 0.4916 - accuracy: 0.8109 - val_loss: 0.5566 - val_accuracy: 0.7375
Epoch 99/100
1/640 [..............................] - ETA: 5s - loss: 0.0038 - accuracy: 1.0000
20/640 [..............................] - ETA: 1s - loss: 0.4598 - accuracy: 0.7000
48/640 [=>............................] - ETA: 1s - loss: 0.5291 - accuracy: 0.7083
77/640 [==>...........................] - ETA: 1s - loss: 0.4976 - accuracy: 0.7532
106/640 [===>..........................] - ETA: 1s - loss: 0.4382 - accuracy: 0.8019
133/640 [=====>........................] - ETA: 0s - loss: 0.4129 - accuracy: 0.8195
161/640 [======>.......................] - ETA: 0s - loss: 0.4489 - accuracy: 0.8199
187/640 [=======>......................] - ETA: 0s - loss: 0.4498 - accuracy: 0.8182
211/640 [========>.....................] - ETA: 0s - loss: 0.4871 - accuracy: 0.8104
232/640 [=========>....................] - ETA: 0s - loss: 0.4861 - accuracy: 0.8147
260/640 [===========>..................] - ETA: 0s - loss: 0.5118 - accuracy: 0.8077
283/640 [============>.................] - ETA: 0s - loss: 0.5405 - accuracy: 0.7986
315/640 [=============>................] - ETA: 0s - loss: 0.5291 - accuracy: 0.8063
337/640 [==============>...............] - ETA: 0s - loss: 0.5218 - accuracy: 0.8071
358/640 [===============>..............] - ETA: 0s - loss: 0.5168 - accuracy: 0.8045
385/640 [=================>............] - ETA: 0s - loss: 0.5057 - accuracy: 0.8078
415/640 [==================>...........] - ETA: 0s - loss: 0.5120 - accuracy: 0.8000
444/640 [===================>..........] - ETA: 0s - loss: 0.5160 - accuracy: 0.8018
471/640 [=====================>........] - ETA: 0s - loss: 0.5112 - accuracy: 0.8004
500/640 [======================>.......] - ETA: 0s - loss: 0.5155 - accuracy: 0.7960
528/640 [=======================>......] - ETA: 0s - loss: 0.5091 - accuracy: 0.8011
556/640 [=========================>....] - ETA: 0s - loss: 0.4996 - accuracy: 0.8058
584/640 [==========================>...] - ETA: 0s - loss: 0.5005 - accuracy: 0.8065
612/640 [===========================>..] - ETA: 0s - loss: 0.4988 - accuracy: 0.8072
636/640 [============================>.] - ETA: 0s - loss: 0.4933 - accuracy: 0.8097
640/640 [==============================] - 2s 3ms/sample - loss: 0.4908 - accuracy: 0.8109 - val_loss: 0.5557 - val_accuracy: 0.7437
Epoch 100/100
1/640 [..............................] - ETA: 1s - loss: 0.0788 - accuracy: 1.0000
24/640 [>.............................] - ETA: 1s - loss: 0.3373 - accuracy: 0.8333
51/640 [=>............................] - ETA: 1s - loss: 0.3640 - accuracy: 0.8235
81/640 [==>...........................] - ETA: 1s - loss: 0.3872 - accuracy: 0.8519
104/640 [===>..........................] - ETA: 1s - loss: 0.4819 - accuracy: 0.8173
129/640 [=====>........................] - ETA: 1s - loss: 0.4632 - accuracy: 0.8295
151/640 [======>.......................] - ETA: 1s - loss: 0.4412 - accuracy: 0.8212
172/640 [=======>......................] - ETA: 0s - loss: 0.4402 - accuracy: 0.8198
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221/640 [=========>....................] - ETA: 0s - loss: 0.4679 - accuracy: 0.8190
245/640 [==========>...................] - ETA: 0s - loss: 0.4734 - accuracy: 0.8163
272/640 [===========>..................] - ETA: 0s - loss: 0.4799 - accuracy: 0.8051
287/640 [============>.................] - ETA: 0s - loss: 0.4823 - accuracy: 0.8014
310/640 [=============>................] - ETA: 0s - loss: 0.4921 - accuracy: 0.8032
335/640 [==============>...............] - ETA: 0s - loss: 0.4783 - accuracy: 0.8119
363/640 [================>.............] - ETA: 0s - loss: 0.4777 - accuracy: 0.8099
389/640 [=================>............] - ETA: 0s - loss: 0.4775 - accuracy: 0.8098
413/640 [==================>...........] - ETA: 0s - loss: 0.4732 - accuracy: 0.8136
438/640 [===================>..........] - ETA: 0s - loss: 0.4653 - accuracy: 0.8219
464/640 [====================>.........] - ETA: 0s - loss: 0.4818 - accuracy: 0.8103
489/640 [=====================>........] - ETA: 0s - loss: 0.4871 - accuracy: 0.8119
515/640 [=======================>......] - ETA: 0s - loss: 0.4713 - accuracy: 0.8194
539/640 [========================>.....] - ETA: 0s - loss: 0.4710 - accuracy: 0.8182
563/640 [=========================>....] - ETA: 0s - loss: 0.4752 - accuracy: 0.8153
590/640 [==========================>...] - ETA: 0s - loss: 0.4883 - accuracy: 0.8051
617/640 [===========================>..] - ETA: 0s - loss: 0.4880 - accuracy: 0.8039
640/640 [==============================] - 2s 4ms/sample - loss: 0.4909 - accuracy: 0.8062 - val_loss: 0.5544 - val_accuracy: 0.7437
200/1 [================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 0s 614us/sample - loss: 0.7118 - accuracy: 0.8050
Confusion Matrix and Statistics
Reference
Prediction 0 1 2 3
0 53 0 2 2
1 0 43 4 6
2 3 0 38 1
3 1 15 5 27
Overall Statistics
Accuracy : 0.805
95% CI : (0.7432, 0.8575)
No Information Rate : 0.29
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.7391
Mcnemar's Test P-Value : NA
Statistics by Class:
Class: 0 Class: 1 Class: 2
Sensitivity 0.9298 0.7414 0.7755
Specificity 0.9720 0.9296 0.9735
Pos Pred Value 0.9298 0.8113 0.9048
Neg Pred Value 0.9720 0.8980 0.9304
Prevalence 0.2850 0.2900 0.2450
Detection Rate 0.2650 0.2150 0.1900
Detection Prevalence 0.2850 0.2650 0.2100
Balanced Accuracy 0.9509 0.8355 0.8745
Class: 3
Sensitivity 0.7500
Specificity 0.8720
Pos Pred Value 0.5625
Neg Pred Value 0.9408
Prevalence 0.1800
Detection Rate 0.1350
Detection Prevalence 0.2400
Balanced Accuracy 0.8110
neural network for batch size: 32
Train on 640 samples, validate on 160 samples
Epoch 1/100
32/640 [>.............................] - ETA: 6s - loss: 1.3288 - accuracy: 0.2188
640/640 [==============================] - 1s 2ms/sample - loss: 1.3104 - accuracy: 0.2656 - val_loss: 1.2971 - val_accuracy: 0.2875
Epoch 2/100
32/640 [>.............................] - ETA: 0s - loss: 1.2776 - accuracy: 0.3125
416/640 [==================>...........] - ETA: 0s - loss: 1.2596 - accuracy: 0.3341
640/640 [==============================] - 1s 858us/sample - loss: 1.2562 - accuracy: 0.3781 - val_loss: 1.2531 - val_accuracy: 0.4688
Epoch 3/100
32/640 [>.............................] - ETA: 0s - loss: 1.2576 - accuracy: 0.4062
640/640 [==============================] - 1s 849us/sample - loss: 1.2100 - accuracy: 0.5266 - val_loss: 1.2129 - val_accuracy: 0.5063
Epoch 4/100
32/640 [>.............................] - ETA: 0s - loss: 1.1377 - accuracy: 0.5938
224/640 [=========>....................] - ETA: 0s - loss: 1.1907 - accuracy: 0.5089
544/640 [========================>.....] - ETA: 0s - loss: 1.1790 - accuracy: 0.5460
640/640 [==============================] - 1s 1ms/sample - loss: 1.1683 - accuracy: 0.5437 - val_loss: 1.1777 - val_accuracy: 0.5125
Epoch 5/100
32/640 [>.............................] - ETA: 0s - loss: 1.1614 - accuracy: 0.5625
640/640 [==============================] - 1s 893us/sample - loss: 1.1305 - accuracy: 0.5594 - val_loss: 1.1465 - val_accuracy: 0.5312
Epoch 6/100
32/640 [>.............................] - ETA: 0s - loss: 1.0879 - accuracy: 0.5938
224/640 [=========>....................] - ETA: 0s - loss: 1.1120 - accuracy: 0.5714
544/640 [========================>.....] - ETA: 0s - loss: 1.1021 - accuracy: 0.5607
640/640 [==============================] - 1s 1ms/sample - loss: 1.0968 - accuracy: 0.5594 - val_loss: 1.1171 - val_accuracy: 0.5375
Epoch 7/100
32/640 [>.............................] - ETA: 0s - loss: 0.9945 - accuracy: 0.7500
640/640 [==============================] - 1s 810us/sample - loss: 1.0656 - accuracy: 0.5656 - val_loss: 1.0899 - val_accuracy: 0.5375
Epoch 8/100
32/640 [>.............................] - ETA: 0s - loss: 1.1806 - accuracy: 0.3750
640/640 [==============================] - 1s 908us/sample - loss: 1.0363 - accuracy: 0.5734 - val_loss: 1.0647 - val_accuracy: 0.5437
Epoch 9/100
32/640 [>.............................] - ETA: 0s - loss: 0.9491 - accuracy: 0.5938
320/640 [==============>...............] - ETA: 0s - loss: 1.0301 - accuracy: 0.5406
640/640 [==============================] - 1s 1ms/sample - loss: 1.0098 - accuracy: 0.5734 - val_loss: 1.0411 - val_accuracy: 0.5625
Epoch 10/100
32/640 [>.............................] - ETA: 0s - loss: 0.9703 - accuracy: 0.6250
640/640 [==============================] - 1s 915us/sample - loss: 0.9852 - accuracy: 0.5781 - val_loss: 1.0183 - val_accuracy: 0.5750
Epoch 11/100
32/640 [>.............................] - ETA: 0s - loss: 0.9731 - accuracy: 0.5312
288/640 [============>.................] - ETA: 0s - loss: 0.9703 - accuracy: 0.5556
640/640 [==============================] - 1s 896us/sample - loss: 0.9621 - accuracy: 0.5828 - val_loss: 0.9971 - val_accuracy: 0.5813
Epoch 12/100
32/640 [>.............................] - ETA: 0s - loss: 0.8482 - accuracy: 0.6250
416/640 [==================>...........] - ETA: 0s - loss: 0.9444 - accuracy: 0.5721
640/640 [==============================] - 1s 1ms/sample - loss: 0.9406 - accuracy: 0.5828 - val_loss: 0.9759 - val_accuracy: 0.5688
Epoch 13/100
32/640 [>.............................] - ETA: 0s - loss: 0.8119 - accuracy: 0.5625
640/640 [==============================] - 1s 834us/sample - loss: 0.9201 - accuracy: 0.5922 - val_loss: 0.9561 - val_accuracy: 0.5813
Epoch 14/100
32/640 [>.............................] - ETA: 0s - loss: 0.9139 - accuracy: 0.5625
576/640 [==========================>...] - ETA: 0s - loss: 0.8992 - accuracy: 0.6059
640/640 [==============================] - 1s 1ms/sample - loss: 0.9012 - accuracy: 0.6062 - val_loss: 0.9366 - val_accuracy: 0.5938
Epoch 15/100
32/640 [>.............................] - ETA: 0s - loss: 0.8066 - accuracy: 0.6250
640/640 [==============================] - 1s 1ms/sample - loss: 0.8833 - accuracy: 0.6250 - val_loss: 0.9178 - val_accuracy: 0.5938
Epoch 16/100
32/640 [>.............................] - ETA: 0s - loss: 0.8210 - accuracy: 0.6250
640/640 [==============================] - 1s 861us/sample - loss: 0.8650 - accuracy: 0.6562 - val_loss: 0.9009 - val_accuracy: 0.5875
Epoch 17/100
32/640 [>.............................] - ETA: 0s - loss: 0.7911 - accuracy: 0.6875
192/640 [========>.....................] - ETA: 0s - loss: 0.8182 - accuracy: 0.6771
448/640 [====================>.........] - ETA: 0s - loss: 0.8486 - accuracy: 0.6853
640/640 [==============================] - 1s 1ms/sample - loss: 0.8492 - accuracy: 0.6844 - val_loss: 0.8843 - val_accuracy: 0.6250
Epoch 18/100
32/640 [>.............................] - ETA: 0s - loss: 0.8631 - accuracy: 0.7188
640/640 [==============================] - 1s 874us/sample - loss: 0.8334 - accuracy: 0.6938 - val_loss: 0.8685 - val_accuracy: 0.6250
Epoch 19/100
32/640 [>.............................] - ETA: 0s - loss: 0.9586 - accuracy: 0.5938
288/640 [============>.................] - ETA: 0s - loss: 0.8404 - accuracy: 0.6562
640/640 [==============================] - 1s 852us/sample - loss: 0.8191 - accuracy: 0.7047 - val_loss: 0.8533 - val_accuracy: 0.6375
Epoch 20/100
32/640 [>.............................] - ETA: 0s - loss: 0.8970 - accuracy: 0.6875
288/640 [============>.................] - ETA: 0s - loss: 0.8558 - accuracy: 0.6944
640/640 [==============================] - 1s 1ms/sample - loss: 0.8049 - accuracy: 0.7094 - val_loss: 0.8391 - val_accuracy: 0.6500
Epoch 21/100
32/640 [>.............................] - ETA: 0s - loss: 0.5667 - accuracy: 0.7500
544/640 [========================>.....] - ETA: 0s - loss: 0.7742 - accuracy: 0.7169
640/640 [==============================] - 1s 2ms/sample - loss: 0.7920 - accuracy: 0.7141 - val_loss: 0.8261 - val_accuracy: 0.6375
Epoch 22/100
32/640 [>.............................] - ETA: 0s - loss: 0.7737 - accuracy: 0.7500
640/640 [==============================] - 0s 775us/sample - loss: 0.7798 - accuracy: 0.7047 - val_loss: 0.8136 - val_accuracy: 0.6438
Epoch 23/100
32/640 [>.............................] - ETA: 0s - loss: 0.5649 - accuracy: 0.8750
544/640 [========================>.....] - ETA: 0s - loss: 0.7743 - accuracy: 0.6930
640/640 [==============================] - 1s 1ms/sample - loss: 0.7689 - accuracy: 0.7000 - val_loss: 0.8020 - val_accuracy: 0.6375
Epoch 24/100
32/640 [>.............................] - ETA: 0s - loss: 0.6620 - accuracy: 0.8125
640/640 [==============================] - 1s 895us/sample - loss: 0.7585 - accuracy: 0.7016 - val_loss: 0.7912 - val_accuracy: 0.6625
Epoch 25/100
32/640 [>.............................] - ETA: 0s - loss: 0.8300 - accuracy: 0.7812
640/640 [==============================] - 1s 883us/sample - loss: 0.7487 - accuracy: 0.6984 - val_loss: 0.7815 - val_accuracy: 0.6562
Epoch 26/100
32/640 [>.............................] - ETA: 0s - loss: 0.7258 - accuracy: 0.6562
224/640 [=========>....................] - ETA: 0s - loss: 0.7842 - accuracy: 0.6920
640/640 [==============================] - 1s 1ms/sample - loss: 0.7404 - accuracy: 0.7094 - val_loss: 0.7719 - val_accuracy: 0.6687
Epoch 27/100
32/640 [>.............................] - ETA: 0s - loss: 0.6696 - accuracy: 0.7500
640/640 [==============================] - 1s 875us/sample - loss: 0.7320 - accuracy: 0.7141 - val_loss: 0.7643 - val_accuracy: 0.6687
Epoch 28/100
32/640 [>.............................] - ETA: 0s - loss: 0.7927 - accuracy: 0.7500
224/640 [=========>....................] - ETA: 0s - loss: 0.7007 - accuracy: 0.7009
640/640 [==============================] - 1s 902us/sample - loss: 0.7243 - accuracy: 0.7188 - val_loss: 0.7562 - val_accuracy: 0.6687
Epoch 29/100
32/640 [>.............................] - ETA: 0s - loss: 0.8467 - accuracy: 0.6250
640/640 [==============================] - 1s 927us/sample - loss: 0.7174 - accuracy: 0.7188 - val_loss: 0.7488 - val_accuracy: 0.6687
Epoch 30/100
32/640 [>.............................] - ETA: 0s - loss: 0.7321 - accuracy: 0.7188
640/640 [==============================] - 1s 897us/sample - loss: 0.7104 - accuracy: 0.7203 - val_loss: 0.7420 - val_accuracy: 0.7000
Epoch 31/100
32/640 [>.............................] - ETA: 0s - loss: 0.4409 - accuracy: 0.8750
256/640 [===========>..................] - ETA: 0s - loss: 0.6425 - accuracy: 0.7734
640/640 [==============================] - 1s 1ms/sample - loss: 0.7046 - accuracy: 0.7391 - val_loss: 0.7363 - val_accuracy: 0.7000
Epoch 32/100
32/640 [>.............................] - ETA: 0s - loss: 1.1709 - accuracy: 0.7188
640/640 [==============================] - 1s 924us/sample - loss: 0.6987 - accuracy: 0.7344 - val_loss: 0.7305 - val_accuracy: 0.7063
Epoch 33/100
32/640 [>.............................] - ETA: 0s - loss: 0.7267 - accuracy: 0.7188
224/640 [=========>....................] - ETA: 0s - loss: 0.6688 - accuracy: 0.7366
640/640 [==============================] - 1s 873us/sample - loss: 0.6932 - accuracy: 0.7312 - val_loss: 0.7242 - val_accuracy: 0.7063
Epoch 34/100
32/640 [>.............................] - ETA: 0s - loss: 0.7668 - accuracy: 0.7812
640/640 [==============================] - 1s 1ms/sample - loss: 0.6880 - accuracy: 0.7359 - val_loss: 0.7190 - val_accuracy: 0.7125
Epoch 35/100
32/640 [>.............................] - ETA: 0s - loss: 0.8161 - accuracy: 0.7812
640/640 [==============================] - 1s 993us/sample - loss: 0.6830 - accuracy: 0.7359 - val_loss: 0.7143 - val_accuracy: 0.7125
Epoch 36/100
32/640 [>.............................] - ETA: 0s - loss: 0.7199 - accuracy: 0.7188
224/640 [=========>....................] - ETA: 0s - loss: 0.7106 - accuracy: 0.7009
608/640 [===========================>..] - ETA: 0s - loss: 0.6796 - accuracy: 0.7336
640/640 [==============================] - 1s 921us/sample - loss: 0.6785 - accuracy: 0.7312 - val_loss: 0.7094 - val_accuracy: 0.7125
Epoch 37/100
32/640 [>.............................] - ETA: 0s - loss: 0.7442 - accuracy: 0.7500
256/640 [===========>..................] - ETA: 0s - loss: 0.7585 - accuracy: 0.7070
608/640 [===========================>..] - ETA: 0s - loss: 0.6747 - accuracy: 0.7418
640/640 [==============================] - 1s 1ms/sample - loss: 0.6741 - accuracy: 0.7422 - val_loss: 0.7050 - val_accuracy: 0.7125
Epoch 38/100
32/640 [>.............................] - ETA: 0s - loss: 0.6470 - accuracy: 0.7500
640/640 [==============================] - 1s 876us/sample - loss: 0.6698 - accuracy: 0.7500 - val_loss: 0.7006 - val_accuracy: 0.7250
Epoch 39/100
32/640 [>.............................] - ETA: 0s - loss: 0.7687 - accuracy: 0.6875
160/640 [======>.......................] - ETA: 0s - loss: 0.6878 - accuracy: 0.7500
544/640 [========================>.....] - ETA: 0s - loss: 0.6746 - accuracy: 0.7426
640/640 [==============================] - 1s 1ms/sample - loss: 0.6658 - accuracy: 0.7484 - val_loss: 0.6969 - val_accuracy: 0.7188
Epoch 40/100
32/640 [>.............................] - ETA: 0s - loss: 0.6545 - accuracy: 0.7188
640/640 [==============================] - 1s 805us/sample - loss: 0.6616 - accuracy: 0.7469 - val_loss: 0.6930 - val_accuracy: 0.7250
Epoch 41/100
32/640 [>.............................] - ETA: 0s - loss: 0.5040 - accuracy: 0.7812
640/640 [==============================] - 1s 842us/sample - loss: 0.6584 - accuracy: 0.7422 - val_loss: 0.6896 - val_accuracy: 0.7250
Epoch 42/100
32/640 [>.............................] - ETA: 0s - loss: 0.9010 - accuracy: 0.8438
640/640 [==============================] - 1s 958us/sample - loss: 0.6547 - accuracy: 0.7453 - val_loss: 0.6861 - val_accuracy: 0.7250
Epoch 43/100
32/640 [>.............................] - ETA: 0s - loss: 0.6528 - accuracy: 0.7500
640/640 [==============================] - 1s 846us/sample - loss: 0.6514 - accuracy: 0.7484 - val_loss: 0.6825 - val_accuracy: 0.7250
Epoch 44/100
32/640 [>.............................] - ETA: 0s - loss: 0.8820 - accuracy: 0.6562
288/640 [============>.................] - ETA: 0s - loss: 0.6697 - accuracy: 0.7639
640/640 [==============================] - 1s 1ms/sample - loss: 0.6482 - accuracy: 0.7500 - val_loss: 0.6797 - val_accuracy: 0.7312
Epoch 45/100
32/640 [>.............................] - ETA: 0s - loss: 0.5634 - accuracy: 0.7812
576/640 [==========================>...] - ETA: 0s - loss: 0.6568 - accuracy: 0.7396
640/640 [==============================] - 1s 945us/sample - loss: 0.6453 - accuracy: 0.7469 - val_loss: 0.6767 - val_accuracy: 0.7312
Epoch 46/100
32/640 [>.............................] - ETA: 0s - loss: 0.5946 - accuracy: 0.6875
640/640 [==============================] - 1s 870us/sample - loss: 0.6426 - accuracy: 0.7531 - val_loss: 0.6736 - val_accuracy: 0.7312
Epoch 47/100
32/640 [>.............................] - ETA: 0s - loss: 0.7287 - accuracy: 0.7188
288/640 [============>.................] - ETA: 0s - loss: 0.6711 - accuracy: 0.7604
640/640 [==============================] - 1s 1ms/sample - loss: 0.6400 - accuracy: 0.7516 - val_loss: 0.6701 - val_accuracy: 0.7312
Epoch 48/100
32/640 [>.............................] - ETA: 0s - loss: 0.6784 - accuracy: 0.7188
640/640 [==============================] - 1s 894us/sample - loss: 0.6373 - accuracy: 0.7531 - val_loss: 0.6677 - val_accuracy: 0.7312
Epoch 49/100
32/640 [>.............................] - ETA: 0s - loss: 0.6099 - accuracy: 0.7500
256/640 [===========>..................] - ETA: 0s - loss: 0.6048 - accuracy: 0.7500
640/640 [==============================] - 1s 1ms/sample - loss: 0.6349 - accuracy: 0.7531 - val_loss: 0.6652 - val_accuracy: 0.7250
Epoch 50/100
32/640 [>.............................] - ETA: 0s - loss: 0.8407 - accuracy: 0.6875
640/640 [==============================] - 1s 856us/sample - loss: 0.6329 - accuracy: 0.7578 - val_loss: 0.6628 - val_accuracy: 0.7312
Epoch 51/100
32/640 [>.............................] - ETA: 0s - loss: 0.8098 - accuracy: 0.5938
640/640 [==============================] - 1s 825us/sample - loss: 0.6308 - accuracy: 0.7531 - val_loss: 0.6606 - val_accuracy: 0.7250
Epoch 52/100
32/640 [>.............................] - ETA: 0s - loss: 0.5536 - accuracy: 0.7188
352/640 [===============>..............] - ETA: 0s - loss: 0.6636 - accuracy: 0.7330
640/640 [==============================] - 1s 1ms/sample - loss: 0.6287 - accuracy: 0.7531 - val_loss: 0.6580 - val_accuracy: 0.7312
Epoch 53/100
32/640 [>.............................] - ETA: 0s - loss: 0.9381 - accuracy: 0.6875
640/640 [==============================] - 1s 910us/sample - loss: 0.6270 - accuracy: 0.7609 - val_loss: 0.6556 - val_accuracy: 0.7312
Epoch 54/100
32/640 [>.............................] - ETA: 0s - loss: 0.5224 - accuracy: 0.8438
352/640 [===============>..............] - ETA: 0s - loss: 0.6005 - accuracy: 0.7443
640/640 [==============================] - 1s 975us/sample - loss: 0.6249 - accuracy: 0.7578 - val_loss: 0.6540 - val_accuracy: 0.7312
Epoch 55/100
32/640 [>.............................] - ETA: 0s - loss: 0.5253 - accuracy: 0.8750
640/640 [==============================] - 1s 869us/sample - loss: 0.6232 - accuracy: 0.7516 - val_loss: 0.6523 - val_accuracy: 0.7250
Epoch 56/100
32/640 [>.............................] - ETA: 0s - loss: 0.5394 - accuracy: 0.7500
416/640 [==================>...........] - ETA: 0s - loss: 0.6255 - accuracy: 0.7500
640/640 [==============================] - 1s 972us/sample - loss: 0.6212 - accuracy: 0.7500 - val_loss: 0.6503 - val_accuracy: 0.7250
Epoch 57/100
32/640 [>.............................] - ETA: 0s - loss: 0.5998 - accuracy: 0.7188
608/640 [===========================>..] - ETA: 0s - loss: 0.6225 - accuracy: 0.7533
640/640 [==============================] - 1s 969us/sample - loss: 0.6197 - accuracy: 0.7531 - val_loss: 0.6484 - val_accuracy: 0.7250
Epoch 58/100
32/640 [>.............................] - ETA: 0s - loss: 0.5713 - accuracy: 0.6875
640/640 [==============================] - 1s 868us/sample - loss: 0.6183 - accuracy: 0.7531 - val_loss: 0.6466 - val_accuracy: 0.7250
Epoch 59/100
32/640 [>.............................] - ETA: 0s - loss: 0.4902 - accuracy: 0.7188
288/640 [============>.................] - ETA: 0s - loss: 0.6332 - accuracy: 0.7604
640/640 [==============================] - 1s 1ms/sample - loss: 0.6170 - accuracy: 0.7625 - val_loss: 0.6448 - val_accuracy: 0.7375
Epoch 60/100
32/640 [>.............................] - ETA: 0s - loss: 0.9314 - accuracy: 0.7812
640/640 [==============================] - 1s 903us/sample - loss: 0.6151 - accuracy: 0.7625 - val_loss: 0.6429 - val_accuracy: 0.7250
Epoch 61/100
32/640 [>.............................] - ETA: 0s - loss: 0.6944 - accuracy: 0.7500
640/640 [==============================] - 1s 807us/sample - loss: 0.6139 - accuracy: 0.7641 - val_loss: 0.6413 - val_accuracy: 0.7312
Epoch 62/100
32/640 [>.............................] - ETA: 0s - loss: 0.6695 - accuracy: 0.6875
192/640 [========>.....................] - ETA: 0s - loss: 0.6276 - accuracy: 0.7344
512/640 [=======================>......] - ETA: 0s - loss: 0.6091 - accuracy: 0.7500
640/640 [==============================] - 1s 1ms/sample - loss: 0.6122 - accuracy: 0.7547 - val_loss: 0.6393 - val_accuracy: 0.7250
Epoch 63/100
32/640 [>.............................] - ETA: 0s - loss: 0.5853 - accuracy: 0.7812
640/640 [==============================] - 1s 905us/sample - loss: 0.6110 - accuracy: 0.7625 - val_loss: 0.6380 - val_accuracy: 0.7250
Epoch 64/100
32/640 [>.............................] - ETA: 0s - loss: 0.7198 - accuracy: 0.7500
640/640 [==============================] - 1s 890us/sample - loss: 0.6098 - accuracy: 0.7563 - val_loss: 0.6369 - val_accuracy: 0.7250
Epoch 65/100
32/640 [>.............................] - ETA: 0s - loss: 0.6337 - accuracy: 0.7188
640/640 [==============================] - 1s 850us/sample - loss: 0.6086 - accuracy: 0.7625 - val_loss: 0.6347 - val_accuracy: 0.7250
Epoch 66/100
32/640 [>.............................] - ETA: 0s - loss: 0.4413 - accuracy: 0.8750
256/640 [===========>..................] - ETA: 0s - loss: 0.6087 - accuracy: 0.7656
640/640 [==============================] - 1s 900us/sample - loss: 0.6074 - accuracy: 0.7625 - val_loss: 0.6336 - val_accuracy: 0.7250
Epoch 67/100
32/640 [>.............................] - ETA: 0s - loss: 0.5668 - accuracy: 0.8125
512/640 [=======================>......] - ETA: 0s - loss: 0.6176 - accuracy: 0.7598
640/640 [==============================] - 1s 1ms/sample - loss: 0.6058 - accuracy: 0.7641 - val_loss: 0.6321 - val_accuracy: 0.7250
Epoch 68/100
32/640 [>.............................] - ETA: 0s - loss: 0.5579 - accuracy: 0.6562
640/640 [==============================] - 1s 875us/sample - loss: 0.6047 - accuracy: 0.7625 - val_loss: 0.6308 - val_accuracy: 0.7188
Epoch 69/100
32/640 [>.............................] - ETA: 0s - loss: 1.2769 - accuracy: 0.5938
448/640 [====================>.........] - ETA: 0s - loss: 0.6325 - accuracy: 0.7545
640/640 [==============================] - 1s 945us/sample - loss: 0.6042 - accuracy: 0.7656 - val_loss: 0.6292 - val_accuracy: 0.7250
Epoch 70/100
32/640 [>.............................] - ETA: 0s - loss: 0.5087 - accuracy: 0.7188
640/640 [==============================] - 1s 809us/sample - loss: 0.6025 - accuracy: 0.7594 - val_loss: 0.6286 - val_accuracy: 0.7188
Epoch 71/100
32/640 [>.............................] - ETA: 0s - loss: 0.4313 - accuracy: 0.8438
256/640 [===========>..................] - ETA: 0s - loss: 0.5397 - accuracy: 0.7812
640/640 [==============================] - 1s 1ms/sample - loss: 0.6016 - accuracy: 0.7656 - val_loss: 0.6276 - val_accuracy: 0.7312
Epoch 72/100
32/640 [>.............................] - ETA: 0s - loss: 0.5534 - accuracy: 0.7812
640/640 [==============================] - 1s 859us/sample - loss: 0.6002 - accuracy: 0.7672 - val_loss: 0.6259 - val_accuracy: 0.7188
Epoch 73/100
32/640 [>.............................] - ETA: 0s - loss: 0.7458 - accuracy: 0.5938
640/640 [==============================] - 1s 850us/sample - loss: 0.5992 - accuracy: 0.7672 - val_loss: 0.6244 - val_accuracy: 0.7250
Epoch 74/100
32/640 [>.............................] - ETA: 0s - loss: 0.6144 - accuracy: 0.7188
256/640 [===========>..................] - ETA: 0s - loss: 0.5786 - accuracy: 0.7852
576/640 [==========================>...] - ETA: 0s - loss: 0.6087 - accuracy: 0.7674
640/640 [==============================] - 1s 1ms/sample - loss: 0.5983 - accuracy: 0.7688 - val_loss: 0.6230 - val_accuracy: 0.7312
Epoch 75/100
32/640 [>.............................] - ETA: 0s - loss: 0.4623 - accuracy: 0.9062
640/640 [==============================] - 1s 783us/sample - loss: 0.5970 - accuracy: 0.7688 - val_loss: 0.6219 - val_accuracy: 0.7312
Epoch 76/100
32/640 [>.............................] - ETA: 0s - loss: 0.6121 - accuracy: 0.8438
608/640 [===========================>..] - ETA: 0s - loss: 0.5982 - accuracy: 0.7697
640/640 [==============================] - 1s 981us/sample - loss: 0.5963 - accuracy: 0.7688 - val_loss: 0.6210 - val_accuracy: 0.7312
Epoch 77/100
32/640 [>.............................] - ETA: 0s - loss: 0.9078 - accuracy: 0.6562
640/640 [==============================] - 1s 872us/sample - loss: 0.5950 - accuracy: 0.7672 - val_loss: 0.6198 - val_accuracy: 0.7312
Epoch 78/100
32/640 [>.............................] - ETA: 0s - loss: 0.5065 - accuracy: 0.8438
224/640 [=========>....................] - ETA: 0s - loss: 0.5216 - accuracy: 0.7723
640/640 [==============================] - 1s 1ms/sample - loss: 0.5944 - accuracy: 0.7688 - val_loss: 0.6191 - val_accuracy: 0.7250
Epoch 79/100
32/640 [>.............................] - ETA: 0s - loss: 0.5722 - accuracy: 0.6562
288/640 [============>.................] - ETA: 0s - loss: 0.5967 - accuracy: 0.7465
608/640 [===========================>..] - ETA: 0s - loss: 0.5866 - accuracy: 0.7697
640/640 [==============================] - 1s 1ms/sample - loss: 0.5935 - accuracy: 0.7672 - val_loss: 0.6171 - val_accuracy: 0.7312
Epoch 80/100
32/640 [>.............................] - ETA: 0s - loss: 0.5708 - accuracy: 0.6875
640/640 [==============================] - 1s 841us/sample - loss: 0.5922 - accuracy: 0.7672 - val_loss: 0.6162 - val_accuracy: 0.7188
Epoch 81/100
32/640 [>.............................] - ETA: 0s - loss: 0.6407 - accuracy: 0.7500
256/640 [===========>..................] - ETA: 0s - loss: 0.6925 - accuracy: 0.7461
640/640 [==============================] - 1s 932us/sample - loss: 0.5916 - accuracy: 0.7688 - val_loss: 0.6147 - val_accuracy: 0.7250
Epoch 82/100
32/640 [>.............................] - ETA: 0s - loss: 0.6039 - accuracy: 0.7500
352/640 [===============>..............] - ETA: 0s - loss: 0.6395 - accuracy: 0.7386
640/640 [==============================] - 1s 950us/sample - loss: 0.5907 - accuracy: 0.7594 - val_loss: 0.6132 - val_accuracy: 0.7188
Epoch 83/100
32/640 [>.............................] - ETA: 0s - loss: 0.5072 - accuracy: 0.7812
640/640 [==============================] - 1s 808us/sample - loss: 0.5897 - accuracy: 0.7703 - val_loss: 0.6126 - val_accuracy: 0.7188
Epoch 84/100
32/640 [>.............................] - ETA: 0s - loss: 0.5369 - accuracy: 0.7188
288/640 [============>.................] - ETA: 0s - loss: 0.5947 - accuracy: 0.7569
608/640 [===========================>..] - ETA: 0s - loss: 0.5941 - accuracy: 0.7664
640/640 [==============================] - 1s 1ms/sample - loss: 0.5887 - accuracy: 0.7688 - val_loss: 0.6117 - val_accuracy: 0.7188
Epoch 85/100
32/640 [>.............................] - ETA: 0s - loss: 0.5197 - accuracy: 0.7500
640/640 [==============================] - 1s 1ms/sample - loss: 0.5881 - accuracy: 0.7688 - val_loss: 0.6108 - val_accuracy: 0.7375
Epoch 86/100
32/640 [>.............................] - ETA: 0s - loss: 0.6874 - accuracy: 0.8125
512/640 [=======================>......] - ETA: 0s - loss: 0.5760 - accuracy: 0.7676
640/640 [==============================] - 1s 835us/sample - loss: 0.5873 - accuracy: 0.7641 - val_loss: 0.6101 - val_accuracy: 0.7312
Epoch 87/100
32/640 [>.............................] - ETA: 0s - loss: 0.6694 - accuracy: 0.6875
512/640 [=======================>......] - ETA: 0s - loss: 0.6008 - accuracy: 0.7656
640/640 [==============================] - 1s 959us/sample - loss: 0.5865 - accuracy: 0.7688 - val_loss: 0.6087 - val_accuracy: 0.7250
Epoch 88/100
32/640 [>.............................] - ETA: 0s - loss: 0.5493 - accuracy: 0.8125
640/640 [==============================] - 1s 797us/sample - loss: 0.5859 - accuracy: 0.7672 - val_loss: 0.6070 - val_accuracy: 0.7250
Epoch 89/100
32/640 [>.............................] - ETA: 0s - loss: 0.7445 - accuracy: 0.7812
160/640 [======>.......................] - ETA: 0s - loss: 0.6482 - accuracy: 0.7688
448/640 [====================>.........] - ETA: 0s - loss: 0.5864 - accuracy: 0.7723
640/640 [==============================] - 1s 1ms/sample - loss: 0.5852 - accuracy: 0.7734 - val_loss: 0.6058 - val_accuracy: 0.7250
Epoch 90/100
32/640 [>.............................] - ETA: 0s - loss: 0.7448 - accuracy: 0.7500
640/640 [==============================] - 1s 834us/sample - loss: 0.5842 - accuracy: 0.7688 - val_loss: 0.6057 - val_accuracy: 0.7250
Epoch 91/100
32/640 [>.............................] - ETA: 0s - loss: 0.5887 - accuracy: 0.8438
640/640 [==============================] - 1s 931us/sample - loss: 0.5834 - accuracy: 0.7672 - val_loss: 0.6047 - val_accuracy: 0.7250
Epoch 92/100
32/640 [>.............................] - ETA: 0s - loss: 0.4648 - accuracy: 0.8438
640/640 [==============================] - 1s 838us/sample - loss: 0.5828 - accuracy: 0.7656 - val_loss: 0.6038 - val_accuracy: 0.7250
Epoch 93/100
32/640 [>.............................] - ETA: 0s - loss: 0.4631 - accuracy: 0.8438
544/640 [========================>.....] - ETA: 0s - loss: 0.5982 - accuracy: 0.7702
640/640 [==============================] - 1s 869us/sample - loss: 0.5821 - accuracy: 0.7703 - val_loss: 0.6027 - val_accuracy: 0.7312
Epoch 94/100
32/640 [>.............................] - ETA: 0s - loss: 0.6782 - accuracy: 0.6875
640/640 [==============================] - 1s 979us/sample - loss: 0.5811 - accuracy: 0.7719 - val_loss: 0.6013 - val_accuracy: 0.7250
Epoch 95/100
32/640 [>.............................] - ETA: 0s - loss: 0.4405 - accuracy: 0.8750
640/640 [==============================] - 1s 830us/sample - loss: 0.5808 - accuracy: 0.7672 - val_loss: 0.6009 - val_accuracy: 0.7312
Epoch 96/100
32/640 [>.............................] - ETA: 0s - loss: 0.4634 - accuracy: 0.7188
288/640 [============>.................] - ETA: 0s - loss: 0.5280 - accuracy: 0.7847
640/640 [==============================] - 1s 999us/sample - loss: 0.5801 - accuracy: 0.7703 - val_loss: 0.5998 - val_accuracy: 0.7250
Epoch 97/100
32/640 [>.............................] - ETA: 0s - loss: 0.5656 - accuracy: 0.7812
640/640 [==============================] - 1s 824us/sample - loss: 0.5792 - accuracy: 0.7719 - val_loss: 0.5986 - val_accuracy: 0.7312
Epoch 98/100
32/640 [>.............................] - ETA: 0s - loss: 0.4338 - accuracy: 0.8438
224/640 [=========>....................] - ETA: 0s - loss: 0.5290 - accuracy: 0.7991
640/640 [==============================] - 1s 1ms/sample - loss: 0.5789 - accuracy: 0.7703 - val_loss: 0.5977 - val_accuracy: 0.7375
Epoch 99/100
32/640 [>.............................] - ETA: 0s - loss: 0.5544 - accuracy: 0.6562
640/640 [==============================] - 1s 848us/sample - loss: 0.5781 - accuracy: 0.7719 - val_loss: 0.5972 - val_accuracy: 0.7312
Epoch 100/100
32/640 [>.............................] - ETA: 0s - loss: 0.5666 - accuracy: 0.8438
640/640 [==============================] - 1s 894us/sample - loss: 0.5773 - accuracy: 0.7703 - val_loss: 0.5971 - val_accuracy: 0.7312
200/1 [================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 0s 628us/sample - loss: 0.9997 - accuracy: 0.7850
Confusion Matrix and Statistics
Reference
Prediction 0 1 2 3
0 56 0 1 0
1 1 41 3 8
2 4 0 37 1
3 2 18 5 23
Overall Statistics
Accuracy : 0.785
95% CI : (0.7215, 0.8398)
No Information Rate : 0.315
P-Value [Acc > NIR] : < 2e-16
Kappa : 0.7115
Mcnemar's Test P-Value : 0.02633
Statistics by Class:
Class: 0 Class: 1 Class: 2
Sensitivity 0.8889 0.6949 0.8043
Specificity 0.9927 0.9149 0.9675
Pos Pred Value 0.9825 0.7736 0.8810
Neg Pred Value 0.9510 0.8776 0.9430
Prevalence 0.3150 0.2950 0.2300
Detection Rate 0.2800 0.2050 0.1850
Detection Prevalence 0.2850 0.2650 0.2100
Balanced Accuracy 0.9408 0.8049 0.8859
Class: 3
Sensitivity 0.7188
Specificity 0.8512
Pos Pred Value 0.4792
Neg Pred Value 0.9408
Prevalence 0.1600
Detection Rate 0.1150
Detection Prevalence 0.2400
Balanced Accuracy 0.7850
neural network for batch size: 64
Train on 640 samples, validate on 160 samples
Epoch 1/100
64/640 [==>...........................] - ETA: 2s - loss: 1.6368 - accuracy: 0.3125
640/640 [==============================] - 1s 2ms/sample - loss: 1.5806 - accuracy: 0.3031 - val_loss: 1.5307 - val_accuracy: 0.2875
Epoch 2/100
64/640 [==>...........................] - ETA: 0s - loss: 1.4680 - accuracy: 0.3594
640/640 [==============================] - 0s 769us/sample - loss: 1.5350 - accuracy: 0.3422 - val_loss: 1.4874 - val_accuracy: 0.3375
Epoch 3/100
64/640 [==>...........................] - ETA: 0s - loss: 1.6188 - accuracy: 0.3281
640/640 [==============================] - 1s 941us/sample - loss: 1.4923 - accuracy: 0.3812 - val_loss: 1.4473 - val_accuracy: 0.3812
Epoch 4/100
64/640 [==>...........................] - ETA: 0s - loss: 1.4005 - accuracy: 0.5000
640/640 [==============================] - 1s 983us/sample - loss: 1.4517 - accuracy: 0.4047 - val_loss: 1.4109 - val_accuracy: 0.3688
Epoch 5/100
64/640 [==>...........................] - ETA: 0s - loss: 1.5110 - accuracy: 0.3125
640/640 [==============================] - 1s 906us/sample - loss: 1.4150 - accuracy: 0.4141 - val_loss: 1.3767 - val_accuracy: 0.4125
Epoch 6/100
64/640 [==>...........................] - ETA: 0s - loss: 1.3420 - accuracy: 0.4375
640/640 [==============================] - 0s 768us/sample - loss: 1.3794 - accuracy: 0.4219 - val_loss: 1.3453 - val_accuracy: 0.4187
Epoch 7/100
64/640 [==>...........................] - ETA: 0s - loss: 1.3404 - accuracy: 0.4375
576/640 [==========================>...] - ETA: 0s - loss: 1.3546 - accuracy: 0.4167
640/640 [==============================] - 1s 1ms/sample - loss: 1.3476 - accuracy: 0.4266 - val_loss: 1.3152 - val_accuracy: 0.4563
Epoch 8/100
64/640 [==>...........................] - ETA: 0s - loss: 1.4054 - accuracy: 0.4062
576/640 [==========================>...] - ETA: 0s - loss: 1.3226 - accuracy: 0.4201
640/640 [==============================] - 1s 861us/sample - loss: 1.3167 - accuracy: 0.4266 - val_loss: 1.2875 - val_accuracy: 0.4688
Epoch 9/100
64/640 [==>...........................] - ETA: 0s - loss: 1.1783 - accuracy: 0.4531
640/640 [==============================] - 1s 819us/sample - loss: 1.2875 - accuracy: 0.4484 - val_loss: 1.2621 - val_accuracy: 0.4812
Epoch 10/100
64/640 [==>...........................] - ETA: 0s - loss: 1.1978 - accuracy: 0.5781
640/640 [==============================] - 1s 843us/sample - loss: 1.2617 - accuracy: 0.4781 - val_loss: 1.2375 - val_accuracy: 0.4938
Epoch 11/100
64/640 [==>...........................] - ETA: 0s - loss: 1.2572 - accuracy: 0.4688
640/640 [==============================] - 1s 817us/sample - loss: 1.2358 - accuracy: 0.4969 - val_loss: 1.2147 - val_accuracy: 0.5000
Epoch 12/100
64/640 [==>...........................] - ETA: 0s - loss: 1.3419 - accuracy: 0.4062
640/640 [==============================] - 0s 753us/sample - loss: 1.2121 - accuracy: 0.5016 - val_loss: 1.1931 - val_accuracy: 0.5000
Epoch 13/100
64/640 [==>...........................] - ETA: 0s - loss: 1.2125 - accuracy: 0.5469
640/640 [==============================] - 1s 788us/sample - loss: 1.1899 - accuracy: 0.5219 - val_loss: 1.1723 - val_accuracy: 0.5188
Epoch 14/100
64/640 [==>...........................] - ETA: 0s - loss: 1.2159 - accuracy: 0.5625
640/640 [==============================] - 1s 844us/sample - loss: 1.1678 - accuracy: 0.5375 - val_loss: 1.1528 - val_accuracy: 0.5562
Epoch 15/100
64/640 [==>...........................] - ETA: 0s - loss: 1.0562 - accuracy: 0.6094
640/640 [==============================] - 1s 897us/sample - loss: 1.1474 - accuracy: 0.5391 - val_loss: 1.1342 - val_accuracy: 0.5750
Epoch 16/100
64/640 [==>...........................] - ETA: 0s - loss: 1.0289 - accuracy: 0.5625
640/640 [==============================] - 0s 726us/sample - loss: 1.1280 - accuracy: 0.5562 - val_loss: 1.1160 - val_accuracy: 0.5813
Epoch 17/100
64/640 [==>...........................] - ETA: 0s - loss: 1.1494 - accuracy: 0.5000
640/640 [==============================] - 1s 804us/sample - loss: 1.1087 - accuracy: 0.5578 - val_loss: 1.0990 - val_accuracy: 0.5938
Epoch 18/100
64/640 [==>...........................] - ETA: 0s - loss: 1.0832 - accuracy: 0.5781
640/640 [==============================] - 0s 743us/sample - loss: 1.0901 - accuracy: 0.5688 - val_loss: 1.0826 - val_accuracy: 0.6062
Epoch 19/100
64/640 [==>...........................] - ETA: 0s - loss: 1.1262 - accuracy: 0.5312
640/640 [==============================] - 1s 781us/sample - loss: 1.0734 - accuracy: 0.5750 - val_loss: 1.0669 - val_accuracy: 0.6125
Epoch 20/100
64/640 [==>...........................] - ETA: 0s - loss: 1.0643 - accuracy: 0.5312
640/640 [==============================] - 0s 755us/sample - loss: 1.0558 - accuracy: 0.5922 - val_loss: 1.0521 - val_accuracy: 0.6187
Epoch 21/100
64/640 [==>...........................] - ETA: 0s - loss: 0.9651 - accuracy: 0.6406
640/640 [==============================] - 1s 800us/sample - loss: 1.0403 - accuracy: 0.6000 - val_loss: 1.0379 - val_accuracy: 0.6250
Epoch 22/100
64/640 [==>...........................] - ETA: 0s - loss: 0.9821 - accuracy: 0.5625
512/640 [=======================>......] - ETA: 0s - loss: 1.0250 - accuracy: 0.6055
640/640 [==============================] - 1s 873us/sample - loss: 1.0250 - accuracy: 0.5969 - val_loss: 1.0241 - val_accuracy: 0.6313
Epoch 23/100
64/640 [==>...........................] - ETA: 0s - loss: 1.0198 - accuracy: 0.5625
640/640 [==============================] - 1s 935us/sample - loss: 1.0098 - accuracy: 0.6047 - val_loss: 1.0112 - val_accuracy: 0.6375
Epoch 24/100
64/640 [==>...........................] - ETA: 0s - loss: 1.0693 - accuracy: 0.6250
640/640 [==============================] - 1s 916us/sample - loss: 0.9958 - accuracy: 0.6125 - val_loss: 0.9985 - val_accuracy: 0.6375
Epoch 25/100
64/640 [==>...........................] - ETA: 0s - loss: 0.9510 - accuracy: 0.5938
640/640 [==============================] - 1s 860us/sample - loss: 0.9822 - accuracy: 0.6156 - val_loss: 0.9864 - val_accuracy: 0.6375
Epoch 26/100
64/640 [==>...........................] - ETA: 0s - loss: 0.9342 - accuracy: 0.6406
640/640 [==============================] - 0s 750us/sample - loss: 0.9690 - accuracy: 0.6187 - val_loss: 0.9751 - val_accuracy: 0.6438
Epoch 27/100
64/640 [==>...........................] - ETA: 0s - loss: 0.9656 - accuracy: 0.5781
640/640 [==============================] - 1s 1ms/sample - loss: 0.9563 - accuracy: 0.6250 - val_loss: 0.9642 - val_accuracy: 0.6438
Epoch 28/100
64/640 [==>...........................] - ETA: 0s - loss: 0.9673 - accuracy: 0.5938
640/640 [==============================] - 1s 812us/sample - loss: 0.9445 - accuracy: 0.6266 - val_loss: 0.9536 - val_accuracy: 0.6500
Epoch 29/100
64/640 [==>...........................] - ETA: 0s - loss: 0.9665 - accuracy: 0.5625
640/640 [==============================] - 1s 959us/sample - loss: 0.9331 - accuracy: 0.6375 - val_loss: 0.9432 - val_accuracy: 0.6562
Epoch 30/100
64/640 [==>...........................] - ETA: 0s - loss: 0.9005 - accuracy: 0.6719
640/640 [==============================] - 1s 997us/sample - loss: 0.9219 - accuracy: 0.6406 - val_loss: 0.9337 - val_accuracy: 0.6500
Epoch 31/100
64/640 [==>...........................] - ETA: 0s - loss: 0.8904 - accuracy: 0.6719
640/640 [==============================] - 0s 735us/sample - loss: 0.9114 - accuracy: 0.6453 - val_loss: 0.9244 - val_accuracy: 0.6500
Epoch 32/100
64/640 [==>...........................] - ETA: 0s - loss: 1.0173 - accuracy: 0.4844
640/640 [==============================] - 1s 783us/sample - loss: 0.9010 - accuracy: 0.6422 - val_loss: 0.9153 - val_accuracy: 0.6625
Epoch 33/100
64/640 [==>...........................] - ETA: 0s - loss: 0.9257 - accuracy: 0.6562
640/640 [==============================] - 0s 745us/sample - loss: 0.8917 - accuracy: 0.6500 - val_loss: 0.9067 - val_accuracy: 0.6687
Epoch 34/100
64/640 [==>...........................] - ETA: 0s - loss: 0.9218 - accuracy: 0.5781
640/640 [==============================] - 1s 900us/sample - loss: 0.8822 - accuracy: 0.6531 - val_loss: 0.8984 - val_accuracy: 0.6687
Epoch 35/100
64/640 [==>...........................] - ETA: 0s - loss: 0.8129 - accuracy: 0.6562
640/640 [==============================] - 0s 740us/sample - loss: 0.8734 - accuracy: 0.6516 - val_loss: 0.8905 - val_accuracy: 0.6625
Epoch 36/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7973 - accuracy: 0.6562
640/640 [==============================] - 1s 788us/sample - loss: 0.8648 - accuracy: 0.6531 - val_loss: 0.8827 - val_accuracy: 0.6625
Epoch 37/100
64/640 [==>...........................] - ETA: 0s - loss: 0.8565 - accuracy: 0.6406
640/640 [==============================] - 1s 807us/sample - loss: 0.8566 - accuracy: 0.6562 - val_loss: 0.8755 - val_accuracy: 0.6687
Epoch 38/100
64/640 [==>...........................] - ETA: 0s - loss: 0.9039 - accuracy: 0.6094
640/640 [==============================] - 0s 781us/sample - loss: 0.8491 - accuracy: 0.6547 - val_loss: 0.8686 - val_accuracy: 0.6750
Epoch 39/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7187 - accuracy: 0.7031
640/640 [==============================] - 1s 786us/sample - loss: 0.8416 - accuracy: 0.6516 - val_loss: 0.8620 - val_accuracy: 0.6750
Epoch 40/100
64/640 [==>...........................] - ETA: 0s - loss: 0.9374 - accuracy: 0.6250
640/640 [==============================] - 0s 778us/sample - loss: 0.8345 - accuracy: 0.6531 - val_loss: 0.8553 - val_accuracy: 0.6687
Epoch 41/100
64/640 [==>...........................] - ETA: 0s - loss: 0.8666 - accuracy: 0.5781
640/640 [==============================] - 0s 762us/sample - loss: 0.8277 - accuracy: 0.6516 - val_loss: 0.8491 - val_accuracy: 0.6750
Epoch 42/100
64/640 [==>...........................] - ETA: 0s - loss: 0.8656 - accuracy: 0.7031
640/640 [==============================] - 0s 757us/sample - loss: 0.8212 - accuracy: 0.6547 - val_loss: 0.8431 - val_accuracy: 0.6687
Epoch 43/100
64/640 [==>...........................] - ETA: 0s - loss: 0.8484 - accuracy: 0.6719
640/640 [==============================] - 0s 774us/sample - loss: 0.8149 - accuracy: 0.6594 - val_loss: 0.8377 - val_accuracy: 0.6687
Epoch 44/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7615 - accuracy: 0.7344
640/640 [==============================] - 0s 745us/sample - loss: 0.8091 - accuracy: 0.6578 - val_loss: 0.8322 - val_accuracy: 0.6750
Epoch 45/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7607 - accuracy: 0.7500
640/640 [==============================] - 1s 1ms/sample - loss: 0.8034 - accuracy: 0.6547 - val_loss: 0.8271 - val_accuracy: 0.6687
Epoch 46/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7307 - accuracy: 0.6406
640/640 [==============================] - 1s 870us/sample - loss: 0.7976 - accuracy: 0.6625 - val_loss: 0.8219 - val_accuracy: 0.6687
Epoch 47/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7501 - accuracy: 0.7031
640/640 [==============================] - 0s 776us/sample - loss: 0.7921 - accuracy: 0.6656 - val_loss: 0.8167 - val_accuracy: 0.6812
Epoch 48/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7660 - accuracy: 0.7031
640/640 [==============================] - 1s 917us/sample - loss: 0.7869 - accuracy: 0.6656 - val_loss: 0.8117 - val_accuracy: 0.6812
Epoch 49/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7756 - accuracy: 0.6406
640/640 [==============================] - 0s 778us/sample - loss: 0.7821 - accuracy: 0.6656 - val_loss: 0.8072 - val_accuracy: 0.6812
Epoch 50/100
64/640 [==>...........................] - ETA: 0s - loss: 0.6846 - accuracy: 0.7656
576/640 [==========================>...] - ETA: 0s - loss: 0.7803 - accuracy: 0.6667
640/640 [==============================] - 1s 819us/sample - loss: 0.7772 - accuracy: 0.6625 - val_loss: 0.8025 - val_accuracy: 0.6750
Epoch 51/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7364 - accuracy: 0.6094
640/640 [==============================] - 1s 795us/sample - loss: 0.7725 - accuracy: 0.6672 - val_loss: 0.7979 - val_accuracy: 0.6750
Epoch 52/100
64/640 [==>...........................] - ETA: 0s - loss: 0.8076 - accuracy: 0.6719
640/640 [==============================] - 1s 888us/sample - loss: 0.7680 - accuracy: 0.6781 - val_loss: 0.7941 - val_accuracy: 0.6812
Epoch 53/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7926 - accuracy: 0.7031
640/640 [==============================] - 0s 754us/sample - loss: 0.7636 - accuracy: 0.6812 - val_loss: 0.7895 - val_accuracy: 0.6750
Epoch 54/100
64/640 [==>...........................] - ETA: 0s - loss: 0.6929 - accuracy: 0.7031
640/640 [==============================] - 1s 812us/sample - loss: 0.7592 - accuracy: 0.6844 - val_loss: 0.7856 - val_accuracy: 0.6625
Epoch 55/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7099 - accuracy: 0.6719
640/640 [==============================] - 0s 747us/sample - loss: 0.7551 - accuracy: 0.6844 - val_loss: 0.7817 - val_accuracy: 0.6812
Epoch 56/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7186 - accuracy: 0.6250
640/640 [==============================] - 1s 826us/sample - loss: 0.7510 - accuracy: 0.6797 - val_loss: 0.7781 - val_accuracy: 0.6812
Epoch 57/100
64/640 [==>...........................] - ETA: 0s - loss: 0.6834 - accuracy: 0.6719
640/640 [==============================] - 0s 777us/sample - loss: 0.7471 - accuracy: 0.6875 - val_loss: 0.7746 - val_accuracy: 0.6750
Epoch 58/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7061 - accuracy: 0.7500
640/640 [==============================] - 1s 800us/sample - loss: 0.7434 - accuracy: 0.6859 - val_loss: 0.7709 - val_accuracy: 0.6812
Epoch 59/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7270 - accuracy: 0.7500
640/640 [==============================] - 0s 743us/sample - loss: 0.7398 - accuracy: 0.6859 - val_loss: 0.7674 - val_accuracy: 0.6812
Epoch 60/100
64/640 [==>...........................] - ETA: 0s - loss: 0.6993 - accuracy: 0.6250
640/640 [==============================] - 1s 820us/sample - loss: 0.7362 - accuracy: 0.6844 - val_loss: 0.7643 - val_accuracy: 0.6875
Epoch 61/100
64/640 [==>...........................] - ETA: 0s - loss: 0.8990 - accuracy: 0.6719
640/640 [==============================] - 0s 773us/sample - loss: 0.7327 - accuracy: 0.6828 - val_loss: 0.7610 - val_accuracy: 0.6938
Epoch 62/100
64/640 [==>...........................] - ETA: 0s - loss: 0.6435 - accuracy: 0.7188
640/640 [==============================] - 0s 733us/sample - loss: 0.7293 - accuracy: 0.6828 - val_loss: 0.7578 - val_accuracy: 0.6938
Epoch 63/100
64/640 [==>...........................] - ETA: 0s - loss: 0.8318 - accuracy: 0.6406
640/640 [==============================] - 0s 777us/sample - loss: 0.7261 - accuracy: 0.6875 - val_loss: 0.7547 - val_accuracy: 0.7063
Epoch 64/100
64/640 [==>...........................] - ETA: 0s - loss: 0.8047 - accuracy: 0.7188
640/640 [==============================] - 0s 726us/sample - loss: 0.7230 - accuracy: 0.6891 - val_loss: 0.7517 - val_accuracy: 0.7063
Epoch 65/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7014 - accuracy: 0.6719
640/640 [==============================] - 1s 868us/sample - loss: 0.7200 - accuracy: 0.6828 - val_loss: 0.7489 - val_accuracy: 0.6875
Epoch 66/100
64/640 [==>...........................] - ETA: 0s - loss: 0.6541 - accuracy: 0.7031
640/640 [==============================] - 0s 759us/sample - loss: 0.7168 - accuracy: 0.6938 - val_loss: 0.7461 - val_accuracy: 0.7000
Epoch 67/100
64/640 [==>...........................] - ETA: 0s - loss: 0.6757 - accuracy: 0.7031
640/640 [==============================] - 0s 768us/sample - loss: 0.7139 - accuracy: 0.6906 - val_loss: 0.7435 - val_accuracy: 0.7000
Epoch 68/100
64/640 [==>...........................] - ETA: 0s - loss: 0.5874 - accuracy: 0.7344
640/640 [==============================] - 0s 780us/sample - loss: 0.7112 - accuracy: 0.6922 - val_loss: 0.7409 - val_accuracy: 0.7000
Epoch 69/100
64/640 [==>...........................] - ETA: 0s - loss: 0.5627 - accuracy: 0.7812
640/640 [==============================] - 0s 724us/sample - loss: 0.7085 - accuracy: 0.6953 - val_loss: 0.7382 - val_accuracy: 0.7000
Epoch 70/100
64/640 [==>...........................] - ETA: 0s - loss: 0.8828 - accuracy: 0.5781
640/640 [==============================] - 1s 857us/sample - loss: 0.7058 - accuracy: 0.6938 - val_loss: 0.7353 - val_accuracy: 0.7000
Epoch 71/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7778 - accuracy: 0.7188
640/640 [==============================] - 0s 744us/sample - loss: 0.7031 - accuracy: 0.6938 - val_loss: 0.7330 - val_accuracy: 0.7000
Epoch 72/100
64/640 [==>...........................] - ETA: 0s - loss: 0.8637 - accuracy: 0.5312
640/640 [==============================] - 1s 865us/sample - loss: 0.7005 - accuracy: 0.6969 - val_loss: 0.7307 - val_accuracy: 0.7000
Epoch 73/100
64/640 [==>...........................] - ETA: 0s - loss: 0.6081 - accuracy: 0.7656
640/640 [==============================] - 0s 721us/sample - loss: 0.6981 - accuracy: 0.6969 - val_loss: 0.7282 - val_accuracy: 0.7000
Epoch 74/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7225 - accuracy: 0.6562
640/640 [==============================] - 1s 846us/sample - loss: 0.6957 - accuracy: 0.6953 - val_loss: 0.7258 - val_accuracy: 0.7000
Epoch 75/100
64/640 [==>...........................] - ETA: 0s - loss: 0.6541 - accuracy: 0.7188
640/640 [==============================] - 0s 740us/sample - loss: 0.6932 - accuracy: 0.6938 - val_loss: 0.7238 - val_accuracy: 0.7000
Epoch 76/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7627 - accuracy: 0.7500
640/640 [==============================] - 0s 772us/sample - loss: 0.6911 - accuracy: 0.6891 - val_loss: 0.7217 - val_accuracy: 0.7000
Epoch 77/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7330 - accuracy: 0.7188
640/640 [==============================] - 0s 725us/sample - loss: 0.6889 - accuracy: 0.6891 - val_loss: 0.7196 - val_accuracy: 0.7000
Epoch 78/100
64/640 [==>...........................] - ETA: 0s - loss: 0.5788 - accuracy: 0.7812
640/640 [==============================] - 1s 830us/sample - loss: 0.6867 - accuracy: 0.6906 - val_loss: 0.7176 - val_accuracy: 0.7000
Epoch 79/100
64/640 [==>...........................] - ETA: 0s - loss: 0.6671 - accuracy: 0.5938
640/640 [==============================] - 0s 763us/sample - loss: 0.6845 - accuracy: 0.6875 - val_loss: 0.7153 - val_accuracy: 0.7000
Epoch 80/100
64/640 [==>...........................] - ETA: 0s - loss: 0.6687 - accuracy: 0.6719
640/640 [==============================] - 1s 907us/sample - loss: 0.6824 - accuracy: 0.6875 - val_loss: 0.7133 - val_accuracy: 0.7000
Epoch 81/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7744 - accuracy: 0.6562
576/640 [==========================>...] - ETA: 0s - loss: 0.6772 - accuracy: 0.6892
640/640 [==============================] - 1s 857us/sample - loss: 0.6805 - accuracy: 0.6891 - val_loss: 0.7109 - val_accuracy: 0.6875
Epoch 82/100
64/640 [==>...........................] - ETA: 0s - loss: 0.5161 - accuracy: 0.8125
640/640 [==============================] - 1s 788us/sample - loss: 0.6785 - accuracy: 0.6969 - val_loss: 0.7090 - val_accuracy: 0.7000
Epoch 83/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7021 - accuracy: 0.6250
640/640 [==============================] - 0s 726us/sample - loss: 0.6765 - accuracy: 0.6938 - val_loss: 0.7074 - val_accuracy: 0.7000
Epoch 84/100
64/640 [==>...........................] - ETA: 0s - loss: 0.5484 - accuracy: 0.7500
640/640 [==============================] - 0s 724us/sample - loss: 0.6745 - accuracy: 0.6969 - val_loss: 0.7057 - val_accuracy: 0.6938
Epoch 85/100
64/640 [==>...........................] - ETA: 0s - loss: 0.6297 - accuracy: 0.7031
640/640 [==============================] - 0s 765us/sample - loss: 0.6727 - accuracy: 0.6969 - val_loss: 0.7040 - val_accuracy: 0.6812
Epoch 86/100
64/640 [==>...........................] - ETA: 0s - loss: 0.5789 - accuracy: 0.7812
640/640 [==============================] - 0s 769us/sample - loss: 0.6709 - accuracy: 0.7000 - val_loss: 0.7024 - val_accuracy: 0.6875
Epoch 87/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7497 - accuracy: 0.7188
640/640 [==============================] - 0s 747us/sample - loss: 0.6690 - accuracy: 0.6953 - val_loss: 0.7006 - val_accuracy: 0.6875
Epoch 88/100
64/640 [==>...........................] - ETA: 0s - loss: 0.6535 - accuracy: 0.6719
640/640 [==============================] - 0s 738us/sample - loss: 0.6674 - accuracy: 0.7016 - val_loss: 0.6985 - val_accuracy: 0.7000
Epoch 89/100
64/640 [==>...........................] - ETA: 0s - loss: 0.6353 - accuracy: 0.7188
640/640 [==============================] - 1s 824us/sample - loss: 0.6655 - accuracy: 0.6984 - val_loss: 0.6971 - val_accuracy: 0.7000
Epoch 90/100
64/640 [==>...........................] - ETA: 0s - loss: 0.8100 - accuracy: 0.6250
640/640 [==============================] - 0s 709us/sample - loss: 0.6639 - accuracy: 0.7000 - val_loss: 0.6954 - val_accuracy: 0.7000
Epoch 91/100
64/640 [==>...........................] - ETA: 0s - loss: 0.6794 - accuracy: 0.6562
640/640 [==============================] - 1s 795us/sample - loss: 0.6622 - accuracy: 0.6984 - val_loss: 0.6934 - val_accuracy: 0.7000
Epoch 92/100
64/640 [==>...........................] - ETA: 0s - loss: 0.6961 - accuracy: 0.7188
640/640 [==============================] - 0s 743us/sample - loss: 0.6607 - accuracy: 0.7047 - val_loss: 0.6915 - val_accuracy: 0.7125
Epoch 93/100
64/640 [==>...........................] - ETA: 0s - loss: 0.6650 - accuracy: 0.6719
640/640 [==============================] - 0s 772us/sample - loss: 0.6591 - accuracy: 0.7016 - val_loss: 0.6907 - val_accuracy: 0.7063
Epoch 94/100
64/640 [==>...........................] - ETA: 0s - loss: 0.6653 - accuracy: 0.7344
640/640 [==============================] - 0s 749us/sample - loss: 0.6572 - accuracy: 0.7047 - val_loss: 0.6889 - val_accuracy: 0.7188
Epoch 95/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7368 - accuracy: 0.6875
576/640 [==========================>...] - ETA: 0s - loss: 0.6568 - accuracy: 0.7083
640/640 [==============================] - 1s 856us/sample - loss: 0.6557 - accuracy: 0.7094 - val_loss: 0.6873 - val_accuracy: 0.7250
Epoch 96/100
64/640 [==>...........................] - ETA: 0s - loss: 0.7307 - accuracy: 0.6562
640/640 [==============================] - 0s 759us/sample - loss: 0.6541 - accuracy: 0.7188 - val_loss: 0.6857 - val_accuracy: 0.7188
Epoch 97/100
64/640 [==>...........................] - ETA: 0s - loss: 0.6650 - accuracy: 0.7188
640/640 [==============================] - 1s 845us/sample - loss: 0.6528 - accuracy: 0.7188 - val_loss: 0.6842 - val_accuracy: 0.7188
Epoch 98/100
64/640 [==>...........................] - ETA: 0s - loss: 0.6831 - accuracy: 0.6719
640/640 [==============================] - 0s 724us/sample - loss: 0.6510 - accuracy: 0.7141 - val_loss: 0.6826 - val_accuracy: 0.7188
Epoch 99/100
64/640 [==>...........................] - ETA: 0s - loss: 0.5005 - accuracy: 0.8125
640/640 [==============================] - 1s 899us/sample - loss: 0.6496 - accuracy: 0.7156 - val_loss: 0.6814 - val_accuracy: 0.7125
Epoch 100/100
64/640 [==>...........................] - ETA: 0s - loss: 0.5884 - accuracy: 0.7031
640/640 [==============================] - 0s 728us/sample - loss: 0.6480 - accuracy: 0.7203 - val_loss: 0.6800 - val_accuracy: 0.7125
200/1 [================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 0s 52us/sample - loss: 0.8708 - accuracy: 0.7450
Confusion Matrix and Statistics
Reference
Prediction 0 1 2 3
0 56 0 1 0
1 1 37 3 12
2 4 3 35 0
3 3 18 6 21
Overall Statistics
Accuracy : 0.745
95% CI : (0.6787, 0.8039)
No Information Rate : 0.32
P-Value [Acc > NIR] : < 2e-16
Kappa : 0.6578
Mcnemar's Test P-Value : 0.04304
Statistics by Class:
Class: 0 Class: 1 Class: 2
Sensitivity 0.8750 0.6379 0.7778
Specificity 0.9926 0.8873 0.9548
Pos Pred Value 0.9825 0.6981 0.8333
Neg Pred Value 0.9441 0.8571 0.9367
Prevalence 0.3200 0.2900 0.2250
Detection Rate 0.2800 0.1850 0.1750
Detection Prevalence 0.2850 0.2650 0.2100
Balanced Accuracy 0.9338 0.7626 0.8663
Class: 3
Sensitivity 0.6364
Specificity 0.8383
Pos Pred Value 0.4375
Neg Pred Value 0.9211
Prevalence 0.1650
Detection Rate 0.1050
Detection Prevalence 0.2400
Balanced Accuracy 0.7373
neural network for batch size: 128
Train on 640 samples, validate on 160 samples
Epoch 1/100
128/640 [=====>........................] - ETA: 1s - loss: 1.5699 - accuracy: 0.1484
640/640 [==============================] - 1s 2ms/sample - loss: 1.4965 - accuracy: 0.2031 - val_loss: 1.5030 - val_accuracy: 0.1813
Epoch 2/100
128/640 [=====>........................] - ETA: 0s - loss: 1.4693 - accuracy: 0.2109
640/640 [==============================] - 1s 848us/sample - loss: 1.4751 - accuracy: 0.2031 - val_loss: 1.4830 - val_accuracy: 0.1875
Epoch 3/100
128/640 [=====>........................] - ETA: 0s - loss: 1.4645 - accuracy: 0.2266
640/640 [==============================] - 1s 1ms/sample - loss: 1.4544 - accuracy: 0.2031 - val_loss: 1.4633 - val_accuracy: 0.1813
Epoch 4/100
128/640 [=====>........................] - ETA: 0s - loss: 1.4120 - accuracy: 0.2266
640/640 [==============================] - 1s 954us/sample - loss: 1.4343 - accuracy: 0.2016 - val_loss: 1.4441 - val_accuracy: 0.1813
Epoch 5/100
128/640 [=====>........................] - ETA: 0s - loss: 1.4490 - accuracy: 0.1641
640/640 [==============================] - 1s 796us/sample - loss: 1.4143 - accuracy: 0.2031 - val_loss: 1.4251 - val_accuracy: 0.1813
Epoch 6/100
128/640 [=====>........................] - ETA: 0s - loss: 1.4164 - accuracy: 0.2422
640/640 [==============================] - 0s 706us/sample - loss: 1.3949 - accuracy: 0.2062 - val_loss: 1.4066 - val_accuracy: 0.1875
Epoch 7/100
128/640 [=====>........................] - ETA: 0s - loss: 1.3610 - accuracy: 0.1875
640/640 [==============================] - 0s 780us/sample - loss: 1.3755 - accuracy: 0.2172 - val_loss: 1.3885 - val_accuracy: 0.2000
Epoch 8/100
128/640 [=====>........................] - ETA: 0s - loss: 1.3381 - accuracy: 0.2344
640/640 [==============================] - 0s 727us/sample - loss: 1.3576 - accuracy: 0.2172 - val_loss: 1.3708 - val_accuracy: 0.2188
Epoch 9/100
128/640 [=====>........................] - ETA: 0s - loss: 1.3457 - accuracy: 0.2344
640/640 [==============================] - 1s 967us/sample - loss: 1.3392 - accuracy: 0.2313 - val_loss: 1.3536 - val_accuracy: 0.2313
Epoch 10/100
128/640 [=====>........................] - ETA: 0s - loss: 1.3213 - accuracy: 0.2031
640/640 [==============================] - 1s 935us/sample - loss: 1.3213 - accuracy: 0.2484 - val_loss: 1.3370 - val_accuracy: 0.2750
Epoch 11/100
128/640 [=====>........................] - ETA: 0s - loss: 1.2949 - accuracy: 0.2891
640/640 [==============================] - 0s 745us/sample - loss: 1.3043 - accuracy: 0.2922 - val_loss: 1.3206 - val_accuracy: 0.2875
Epoch 12/100
128/640 [=====>........................] - ETA: 0s - loss: 1.2939 - accuracy: 0.3750
640/640 [==============================] - 0s 699us/sample - loss: 1.2876 - accuracy: 0.3297 - val_loss: 1.3046 - val_accuracy: 0.3125
Epoch 13/100
128/640 [=====>........................] - ETA: 0s - loss: 1.3674 - accuracy: 0.2812
640/640 [==============================] - 1s 903us/sample - loss: 1.2712 - accuracy: 0.3516 - val_loss: 1.2889 - val_accuracy: 0.3187
Epoch 14/100
128/640 [=====>........................] - ETA: 0s - loss: 1.2584 - accuracy: 0.3828
640/640 [==============================] - 1s 793us/sample - loss: 1.2555 - accuracy: 0.3625 - val_loss: 1.2734 - val_accuracy: 0.3187
Epoch 15/100
128/640 [=====>........................] - ETA: 0s - loss: 1.2191 - accuracy: 0.3672
640/640 [==============================] - 0s 769us/sample - loss: 1.2400 - accuracy: 0.3656 - val_loss: 1.2585 - val_accuracy: 0.3375
Epoch 16/100
128/640 [=====>........................] - ETA: 0s - loss: 1.2114 - accuracy: 0.4219
640/640 [==============================] - 0s 693us/sample - loss: 1.2246 - accuracy: 0.3781 - val_loss: 1.2440 - val_accuracy: 0.3500
Epoch 17/100
128/640 [=====>........................] - ETA: 0s - loss: 1.2312 - accuracy: 0.3906
640/640 [==============================] - 0s 744us/sample - loss: 1.2100 - accuracy: 0.3797 - val_loss: 1.2298 - val_accuracy: 0.3500
Epoch 18/100
128/640 [=====>........................] - ETA: 0s - loss: 1.2316 - accuracy: 0.2734
640/640 [==============================] - 0s 704us/sample - loss: 1.1958 - accuracy: 0.3812 - val_loss: 1.2158 - val_accuracy: 0.3562
Epoch 19/100
128/640 [=====>........................] - ETA: 0s - loss: 1.1325 - accuracy: 0.4375
640/640 [==============================] - 1s 901us/sample - loss: 1.1818 - accuracy: 0.3906 - val_loss: 1.2026 - val_accuracy: 0.3875
Epoch 20/100
128/640 [=====>........................] - ETA: 0s - loss: 1.1630 - accuracy: 0.4297
640/640 [==============================] - 0s 707us/sample - loss: 1.1679 - accuracy: 0.4266 - val_loss: 1.1897 - val_accuracy: 0.4500
Epoch 21/100
128/640 [=====>........................] - ETA: 0s - loss: 1.1362 - accuracy: 0.4688
640/640 [==============================] - 0s 745us/sample - loss: 1.1553 - accuracy: 0.5219 - val_loss: 1.1770 - val_accuracy: 0.5312
Epoch 22/100
128/640 [=====>........................] - ETA: 0s - loss: 1.1608 - accuracy: 0.5391
640/640 [==============================] - 0s 710us/sample - loss: 1.1418 - accuracy: 0.5906 - val_loss: 1.1647 - val_accuracy: 0.5813
Epoch 23/100
128/640 [=====>........................] - ETA: 0s - loss: 1.1530 - accuracy: 0.5703
640/640 [==============================] - 1s 923us/sample - loss: 1.1294 - accuracy: 0.6156 - val_loss: 1.1528 - val_accuracy: 0.6000
Epoch 24/100
128/640 [=====>........................] - ETA: 0s - loss: 1.1201 - accuracy: 0.6172
640/640 [==============================] - 0s 714us/sample - loss: 1.1173 - accuracy: 0.6234 - val_loss: 1.1414 - val_accuracy: 0.5938
Epoch 25/100
128/640 [=====>........................] - ETA: 0s - loss: 1.1250 - accuracy: 0.6094
640/640 [==============================] - 0s 711us/sample - loss: 1.1054 - accuracy: 0.6250 - val_loss: 1.1303 - val_accuracy: 0.5938
Epoch 26/100
128/640 [=====>........................] - ETA: 0s - loss: 1.0955 - accuracy: 0.5781
640/640 [==============================] - 0s 732us/sample - loss: 1.0942 - accuracy: 0.6234 - val_loss: 1.1194 - val_accuracy: 0.5875
Epoch 27/100
128/640 [=====>........................] - ETA: 0s - loss: 1.0849 - accuracy: 0.6016
640/640 [==============================] - 0s 710us/sample - loss: 1.0830 - accuracy: 0.6234 - val_loss: 1.1089 - val_accuracy: 0.5875
Epoch 28/100
128/640 [=====>........................] - ETA: 0s - loss: 1.0545 - accuracy: 0.6484
640/640 [==============================] - 0s 716us/sample - loss: 1.0721 - accuracy: 0.6250 - val_loss: 1.0989 - val_accuracy: 0.5875
Epoch 29/100
128/640 [=====>........................] - ETA: 0s - loss: 1.1329 - accuracy: 0.6328
640/640 [==============================] - 1s 875us/sample - loss: 1.0620 - accuracy: 0.6250 - val_loss: 1.0891 - val_accuracy: 0.5875
Epoch 30/100
128/640 [=====>........................] - ETA: 0s - loss: 1.0673 - accuracy: 0.6562
640/640 [==============================] - 0s 723us/sample - loss: 1.0521 - accuracy: 0.6266 - val_loss: 1.0797 - val_accuracy: 0.5875
Epoch 31/100
128/640 [=====>........................] - ETA: 0s - loss: 1.1021 - accuracy: 0.6250
640/640 [==============================] - 0s 739us/sample - loss: 1.0423 - accuracy: 0.6281 - val_loss: 1.0707 - val_accuracy: 0.5875
Epoch 32/100
128/640 [=====>........................] - ETA: 0s - loss: 0.9995 - accuracy: 0.6797
640/640 [==============================] - 0s 724us/sample - loss: 1.0333 - accuracy: 0.6297 - val_loss: 1.0615 - val_accuracy: 0.5875
Epoch 33/100
128/640 [=====>........................] - ETA: 0s - loss: 1.0861 - accuracy: 0.5234
640/640 [==============================] - 1s 792us/sample - loss: 1.0239 - accuracy: 0.6328 - val_loss: 1.0528 - val_accuracy: 0.5938
Epoch 34/100
128/640 [=====>........................] - ETA: 0s - loss: 0.9931 - accuracy: 0.6406
640/640 [==============================] - 0s 691us/sample - loss: 1.0153 - accuracy: 0.6359 - val_loss: 1.0443 - val_accuracy: 0.6062
Epoch 35/100
128/640 [=====>........................] - ETA: 0s - loss: 1.0051 - accuracy: 0.6172
640/640 [==============================] - 0s 683us/sample - loss: 1.0066 - accuracy: 0.6359 - val_loss: 1.0362 - val_accuracy: 0.6062
Epoch 36/100
128/640 [=====>........................] - ETA: 0s - loss: 0.9761 - accuracy: 0.6484
640/640 [==============================] - 0s 709us/sample - loss: 0.9983 - accuracy: 0.6359 - val_loss: 1.0282 - val_accuracy: 0.6062
Epoch 37/100
128/640 [=====>........................] - ETA: 0s - loss: 0.9704 - accuracy: 0.6250
640/640 [==============================] - 0s 726us/sample - loss: 0.9904 - accuracy: 0.6406 - val_loss: 1.0203 - val_accuracy: 0.6062
Epoch 38/100
128/640 [=====>........................] - ETA: 0s - loss: 0.9503 - accuracy: 0.6562
640/640 [==============================] - 0s 730us/sample - loss: 0.9826 - accuracy: 0.6453 - val_loss: 1.0126 - val_accuracy: 0.6125
Epoch 39/100
128/640 [=====>........................] - ETA: 0s - loss: 0.9730 - accuracy: 0.6719
640/640 [==============================] - 0s 725us/sample - loss: 0.9748 - accuracy: 0.6453 - val_loss: 1.0051 - val_accuracy: 0.6125
Epoch 40/100
128/640 [=====>........................] - ETA: 0s - loss: 0.9616 - accuracy: 0.6484
640/640 [==============================] - 0s 780us/sample - loss: 0.9673 - accuracy: 0.6500 - val_loss: 0.9980 - val_accuracy: 0.6187
Epoch 41/100
128/640 [=====>........................] - ETA: 0s - loss: 0.9935 - accuracy: 0.6562
640/640 [==============================] - 0s 709us/sample - loss: 0.9602 - accuracy: 0.6500 - val_loss: 0.9909 - val_accuracy: 0.6250
Epoch 42/100
128/640 [=====>........................] - ETA: 0s - loss: 0.9472 - accuracy: 0.6484
640/640 [==============================] - 0s 745us/sample - loss: 0.9532 - accuracy: 0.6547 - val_loss: 0.9840 - val_accuracy: 0.6187
Epoch 43/100
128/640 [=====>........................] - ETA: 0s - loss: 0.9542 - accuracy: 0.5781
640/640 [==============================] - 0s 700us/sample - loss: 0.9462 - accuracy: 0.6562 - val_loss: 0.9773 - val_accuracy: 0.6250
Epoch 44/100
128/640 [=====>........................] - ETA: 0s - loss: 0.9772 - accuracy: 0.6875
640/640 [==============================] - 0s 746us/sample - loss: 0.9396 - accuracy: 0.6562 - val_loss: 0.9709 - val_accuracy: 0.6313
Epoch 45/100
128/640 [=====>........................] - ETA: 0s - loss: 1.0231 - accuracy: 0.6172
640/640 [==============================] - 0s 749us/sample - loss: 0.9330 - accuracy: 0.6594 - val_loss: 0.9644 - val_accuracy: 0.6375
Epoch 46/100
128/640 [=====>........................] - ETA: 0s - loss: 0.9549 - accuracy: 0.6094
640/640 [==============================] - 1s 817us/sample - loss: 0.9268 - accuracy: 0.6672 - val_loss: 0.9583 - val_accuracy: 0.6438
Epoch 47/100
128/640 [=====>........................] - ETA: 0s - loss: 0.9003 - accuracy: 0.7344
640/640 [==============================] - 0s 700us/sample - loss: 0.9206 - accuracy: 0.6687 - val_loss: 0.9522 - val_accuracy: 0.6438
Epoch 48/100
128/640 [=====>........................] - ETA: 0s - loss: 1.0221 - accuracy: 0.6797
640/640 [==============================] - 0s 750us/sample - loss: 0.9144 - accuracy: 0.6687 - val_loss: 0.9462 - val_accuracy: 0.6500
Epoch 49/100
128/640 [=====>........................] - ETA: 0s - loss: 0.9264 - accuracy: 0.6562
640/640 [==============================] - 0s 728us/sample - loss: 0.9088 - accuracy: 0.6703 - val_loss: 0.9403 - val_accuracy: 0.6562
Epoch 50/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8916 - accuracy: 0.6328
640/640 [==============================] - 0s 710us/sample - loss: 0.9030 - accuracy: 0.6687 - val_loss: 0.9347 - val_accuracy: 0.6562
Epoch 51/100
128/640 [=====>........................] - ETA: 0s - loss: 0.9516 - accuracy: 0.6484
640/640 [==============================] - 0s 715us/sample - loss: 0.8972 - accuracy: 0.6734 - val_loss: 0.9292 - val_accuracy: 0.6562
Epoch 52/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8903 - accuracy: 0.6484
640/640 [==============================] - 0s 709us/sample - loss: 0.8918 - accuracy: 0.6781 - val_loss: 0.9236 - val_accuracy: 0.6562
Epoch 53/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8585 - accuracy: 0.7188
640/640 [==============================] - 0s 726us/sample - loss: 0.8865 - accuracy: 0.6781 - val_loss: 0.9182 - val_accuracy: 0.6562
Epoch 54/100
128/640 [=====>........................] - ETA: 0s - loss: 0.9451 - accuracy: 0.6875
640/640 [==============================] - 0s 702us/sample - loss: 0.8810 - accuracy: 0.6781 - val_loss: 0.9131 - val_accuracy: 0.6500
Epoch 55/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8425 - accuracy: 0.6875
640/640 [==============================] - 0s 718us/sample - loss: 0.8762 - accuracy: 0.6828 - val_loss: 0.9080 - val_accuracy: 0.6500
Epoch 56/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8956 - accuracy: 0.6406
640/640 [==============================] - 0s 716us/sample - loss: 0.8711 - accuracy: 0.6828 - val_loss: 0.9030 - val_accuracy: 0.6500
Epoch 57/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8616 - accuracy: 0.6953
640/640 [==============================] - 0s 761us/sample - loss: 0.8661 - accuracy: 0.6828 - val_loss: 0.8982 - val_accuracy: 0.6562
Epoch 58/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8807 - accuracy: 0.6797
640/640 [==============================] - 0s 770us/sample - loss: 0.8615 - accuracy: 0.6844 - val_loss: 0.8935 - val_accuracy: 0.6500
Epoch 59/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8488 - accuracy: 0.6562
640/640 [==============================] - 0s 735us/sample - loss: 0.8569 - accuracy: 0.6875 - val_loss: 0.8887 - val_accuracy: 0.6625
Epoch 60/100
128/640 [=====>........................] - ETA: 0s - loss: 0.9802 - accuracy: 0.5938
640/640 [==============================] - 0s 702us/sample - loss: 0.8522 - accuracy: 0.6922 - val_loss: 0.8840 - val_accuracy: 0.6625
Epoch 61/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8751 - accuracy: 0.6562
640/640 [==============================] - 0s 698us/sample - loss: 0.8478 - accuracy: 0.6922 - val_loss: 0.8795 - val_accuracy: 0.6625
Epoch 62/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8220 - accuracy: 0.6875
640/640 [==============================] - 0s 737us/sample - loss: 0.8437 - accuracy: 0.6984 - val_loss: 0.8750 - val_accuracy: 0.6625
Epoch 63/100
128/640 [=====>........................] - ETA: 0s - loss: 0.9454 - accuracy: 0.6719
640/640 [==============================] - 0s 690us/sample - loss: 0.8390 - accuracy: 0.6969 - val_loss: 0.8709 - val_accuracy: 0.6625
Epoch 64/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8308 - accuracy: 0.6875
640/640 [==============================] - 0s 729us/sample - loss: 0.8349 - accuracy: 0.6969 - val_loss: 0.8667 - val_accuracy: 0.6562
Epoch 65/100
128/640 [=====>........................] - ETA: 0s - loss: 0.7971 - accuracy: 0.7031
640/640 [==============================] - 1s 796us/sample - loss: 0.8308 - accuracy: 0.7000 - val_loss: 0.8627 - val_accuracy: 0.6562
Epoch 66/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8459 - accuracy: 0.7031
640/640 [==============================] - 0s 765us/sample - loss: 0.8267 - accuracy: 0.7000 - val_loss: 0.8586 - val_accuracy: 0.6562
Epoch 67/100
128/640 [=====>........................] - ETA: 0s - loss: 0.7465 - accuracy: 0.7812
640/640 [==============================] - 0s 686us/sample - loss: 0.8230 - accuracy: 0.7047 - val_loss: 0.8547 - val_accuracy: 0.6500
Epoch 68/100
128/640 [=====>........................] - ETA: 0s - loss: 0.9485 - accuracy: 0.6719
640/640 [==============================] - 0s 721us/sample - loss: 0.8190 - accuracy: 0.7094 - val_loss: 0.8508 - val_accuracy: 0.6500
Epoch 69/100
128/640 [=====>........................] - ETA: 0s - loss: 0.7541 - accuracy: 0.7344
640/640 [==============================] - 0s 725us/sample - loss: 0.8154 - accuracy: 0.7094 - val_loss: 0.8470 - val_accuracy: 0.6500
Epoch 70/100
128/640 [=====>........................] - ETA: 0s - loss: 0.7808 - accuracy: 0.7031
640/640 [==============================] - 0s 751us/sample - loss: 0.8115 - accuracy: 0.7109 - val_loss: 0.8434 - val_accuracy: 0.6500
Epoch 71/100
128/640 [=====>........................] - ETA: 0s - loss: 0.7522 - accuracy: 0.7266
640/640 [==============================] - 0s 773us/sample - loss: 0.8080 - accuracy: 0.7125 - val_loss: 0.8397 - val_accuracy: 0.6562
Epoch 72/100
128/640 [=====>........................] - ETA: 0s - loss: 0.7544 - accuracy: 0.7031
640/640 [==============================] - 0s 738us/sample - loss: 0.8044 - accuracy: 0.7141 - val_loss: 0.8361 - val_accuracy: 0.6562
Epoch 73/100
128/640 [=====>........................] - ETA: 0s - loss: 0.7191 - accuracy: 0.7578
640/640 [==============================] - 1s 927us/sample - loss: 0.8009 - accuracy: 0.7172 - val_loss: 0.8328 - val_accuracy: 0.6562
Epoch 74/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8995 - accuracy: 0.6797
640/640 [==============================] - 0s 708us/sample - loss: 0.7976 - accuracy: 0.7172 - val_loss: 0.8294 - val_accuracy: 0.6562
Epoch 75/100
128/640 [=====>........................] - ETA: 0s - loss: 0.7826 - accuracy: 0.6953
640/640 [==============================] - 1s 887us/sample - loss: 0.7943 - accuracy: 0.7172 - val_loss: 0.8259 - val_accuracy: 0.6562
Epoch 76/100
128/640 [=====>........................] - ETA: 0s - loss: 0.7858 - accuracy: 0.6719
640/640 [==============================] - 0s 725us/sample - loss: 0.7910 - accuracy: 0.7172 - val_loss: 0.8226 - val_accuracy: 0.6500
Epoch 77/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8147 - accuracy: 0.6875
640/640 [==============================] - 0s 707us/sample - loss: 0.7879 - accuracy: 0.7203 - val_loss: 0.8194 - val_accuracy: 0.6500
Epoch 78/100
128/640 [=====>........................] - ETA: 0s - loss: 0.7425 - accuracy: 0.7422
640/640 [==============================] - 0s 738us/sample - loss: 0.7847 - accuracy: 0.7250 - val_loss: 0.8162 - val_accuracy: 0.6625
Epoch 79/100
128/640 [=====>........................] - ETA: 0s - loss: 0.7442 - accuracy: 0.8047
640/640 [==============================] - 0s 748us/sample - loss: 0.7817 - accuracy: 0.7266 - val_loss: 0.8131 - val_accuracy: 0.6562
Epoch 80/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8216 - accuracy: 0.6797
640/640 [==============================] - 0s 771us/sample - loss: 0.7786 - accuracy: 0.7281 - val_loss: 0.8100 - val_accuracy: 0.6687
Epoch 81/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8110 - accuracy: 0.7422
640/640 [==============================] - 0s 717us/sample - loss: 0.7756 - accuracy: 0.7328 - val_loss: 0.8071 - val_accuracy: 0.6625
Epoch 82/100
128/640 [=====>........................] - ETA: 0s - loss: 0.6865 - accuracy: 0.7734
640/640 [==============================] - 0s 718us/sample - loss: 0.7728 - accuracy: 0.7328 - val_loss: 0.8043 - val_accuracy: 0.6750
Epoch 83/100
128/640 [=====>........................] - ETA: 0s - loss: 0.7413 - accuracy: 0.7266
640/640 [==============================] - 0s 740us/sample - loss: 0.7701 - accuracy: 0.7328 - val_loss: 0.8015 - val_accuracy: 0.6625
Epoch 84/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8428 - accuracy: 0.7344
640/640 [==============================] - 0s 726us/sample - loss: 0.7672 - accuracy: 0.7328 - val_loss: 0.7987 - val_accuracy: 0.6687
Epoch 85/100
128/640 [=====>........................] - ETA: 0s - loss: 0.7004 - accuracy: 0.7578
640/640 [==============================] - 0s 700us/sample - loss: 0.7645 - accuracy: 0.7312 - val_loss: 0.7959 - val_accuracy: 0.6812
Epoch 86/100
128/640 [=====>........................] - ETA: 0s - loss: 0.6922 - accuracy: 0.7500
640/640 [==============================] - 1s 888us/sample - loss: 0.7618 - accuracy: 0.7344 - val_loss: 0.7931 - val_accuracy: 0.6875
Epoch 87/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8109 - accuracy: 0.7500
640/640 [==============================] - 0s 692us/sample - loss: 0.7591 - accuracy: 0.7344 - val_loss: 0.7906 - val_accuracy: 0.6875
Epoch 88/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8013 - accuracy: 0.7422
640/640 [==============================] - 0s 746us/sample - loss: 0.7565 - accuracy: 0.7344 - val_loss: 0.7881 - val_accuracy: 0.6875
Epoch 89/100
128/640 [=====>........................] - ETA: 0s - loss: 0.6788 - accuracy: 0.7188
640/640 [==============================] - 0s 744us/sample - loss: 0.7540 - accuracy: 0.7375 - val_loss: 0.7855 - val_accuracy: 0.6938
Epoch 90/100
128/640 [=====>........................] - ETA: 0s - loss: 0.7978 - accuracy: 0.7266
640/640 [==============================] - 1s 793us/sample - loss: 0.7514 - accuracy: 0.7359 - val_loss: 0.7829 - val_accuracy: 0.6938
Epoch 91/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8114 - accuracy: 0.7188
640/640 [==============================] - 0s 691us/sample - loss: 0.7490 - accuracy: 0.7344 - val_loss: 0.7804 - val_accuracy: 0.6938
Epoch 92/100
128/640 [=====>........................] - ETA: 0s - loss: 0.6826 - accuracy: 0.7734
640/640 [==============================] - 0s 764us/sample - loss: 0.7467 - accuracy: 0.7344 - val_loss: 0.7779 - val_accuracy: 0.7000
Epoch 93/100
128/640 [=====>........................] - ETA: 0s - loss: 0.7296 - accuracy: 0.7266
640/640 [==============================] - 0s 720us/sample - loss: 0.7443 - accuracy: 0.7344 - val_loss: 0.7756 - val_accuracy: 0.7000
Epoch 94/100
128/640 [=====>........................] - ETA: 0s - loss: 0.6434 - accuracy: 0.7969
640/640 [==============================] - 0s 706us/sample - loss: 0.7420 - accuracy: 0.7344 - val_loss: 0.7732 - val_accuracy: 0.7000
Epoch 95/100
128/640 [=====>........................] - ETA: 0s - loss: 0.7694 - accuracy: 0.7344
640/640 [==============================] - 0s 727us/sample - loss: 0.7398 - accuracy: 0.7344 - val_loss: 0.7708 - val_accuracy: 0.7000
Epoch 96/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8818 - accuracy: 0.7031
640/640 [==============================] - 0s 726us/sample - loss: 0.7374 - accuracy: 0.7344 - val_loss: 0.7685 - val_accuracy: 0.7063
Epoch 97/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8350 - accuracy: 0.6406
640/640 [==============================] - 0s 736us/sample - loss: 0.7353 - accuracy: 0.7359 - val_loss: 0.7662 - val_accuracy: 0.7063
Epoch 98/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8314 - accuracy: 0.6719
640/640 [==============================] - 0s 707us/sample - loss: 0.7331 - accuracy: 0.7344 - val_loss: 0.7641 - val_accuracy: 0.7063
Epoch 99/100
128/640 [=====>........................] - ETA: 0s - loss: 0.8408 - accuracy: 0.7344
640/640 [==============================] - 1s 840us/sample - loss: 0.7311 - accuracy: 0.7344 - val_loss: 0.7620 - val_accuracy: 0.7063
Epoch 100/100
128/640 [=====>........................] - ETA: 0s - loss: 0.7141 - accuracy: 0.7266
640/640 [==============================] - 0s 707us/sample - loss: 0.7289 - accuracy: 0.7344 - val_loss: 0.7599 - val_accuracy: 0.7125
Time taken to train model: 49.1198689937592
200/1 [================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 0s 40us/sample - loss: 0.7939 - accuracy: 0.7300
Confusion Matrix and Statistics
Reference
Prediction 0 1 2 3
0 56 0 1 0
1 2 35 3 13
2 5 2 33 2
3 5 16 5 22
Overall Statistics
Accuracy : 0.73
95% CI : (0.6628, 0.7902)
No Information Rate : 0.34
P-Value [Acc > NIR] : < 2e-16
Kappa : 0.6373
Mcnemar's Test P-Value : 0.07509
Statistics by Class:
Class: 0 Class: 1 Class: 2
Sensitivity 0.8235 0.6604 0.7857
Specificity 0.9924 0.8776 0.9430
Pos Pred Value 0.9825 0.6604 0.7857
Neg Pred Value 0.9161 0.8776 0.9430
Prevalence 0.3400 0.2650 0.2100
Detection Rate 0.2800 0.1750 0.1650
Detection Prevalence 0.2850 0.2650 0.2100
Balanced Accuracy 0.9080 0.7690 0.8644
Class: 3
Sensitivity 0.5946
Specificity 0.8405
Pos Pred Value 0.4583
Neg Pred Value 0.9013
Prevalence 0.1850
Detection Rate 0.1100
Detection Prevalence 0.2400
Balanced Accuracy 0.7175
neural network for batch size: 256
Train on 640 samples, validate on 160 samples
Epoch 1/100
256/640 [===========>..................] - ETA: 0s - loss: 1.4997 - accuracy: 0.0273
640/640 [==============================] - 1s 2ms/sample - loss: 1.4959 - accuracy: 0.0328 - val_loss: 1.5040 - val_accuracy: 0.0250
Epoch 2/100
256/640 [===========>..................] - ETA: 0s - loss: 1.4987 - accuracy: 0.0391
640/640 [==============================] - 1s 867us/sample - loss: 1.4848 - accuracy: 0.0328 - val_loss: 1.4932 - val_accuracy: 0.0250
Epoch 3/100
256/640 [===========>..................] - ETA: 0s - loss: 1.4890 - accuracy: 0.0391
640/640 [==============================] - 0s 706us/sample - loss: 1.4737 - accuracy: 0.0359 - val_loss: 1.4827 - val_accuracy: 0.0312
Epoch 4/100
256/640 [===========>..................] - ETA: 0s - loss: 1.4586 - accuracy: 0.0312
640/640 [==============================] - 0s 727us/sample - loss: 1.4627 - accuracy: 0.0359 - val_loss: 1.4722 - val_accuracy: 0.0312
Epoch 5/100
256/640 [===========>..................] - ETA: 0s - loss: 1.4598 - accuracy: 0.0352
640/640 [==============================] - 0s 748us/sample - loss: 1.4518 - accuracy: 0.0391 - val_loss: 1.4619 - val_accuracy: 0.0312
Epoch 6/100
256/640 [===========>..................] - ETA: 0s - loss: 1.4324 - accuracy: 0.0469
640/640 [==============================] - 0s 707us/sample - loss: 1.4416 - accuracy: 0.0437 - val_loss: 1.4518 - val_accuracy: 0.0375
Epoch 7/100
256/640 [===========>..................] - ETA: 0s - loss: 1.4353 - accuracy: 0.0469
640/640 [==============================] - 0s 765us/sample - loss: 1.4311 - accuracy: 0.0453 - val_loss: 1.4419 - val_accuracy: 0.0375
Epoch 8/100
256/640 [===========>..................] - ETA: 0s - loss: 1.4284 - accuracy: 0.0547
640/640 [==============================] - 0s 731us/sample - loss: 1.4208 - accuracy: 0.0469 - val_loss: 1.4322 - val_accuracy: 0.0437
Epoch 9/100
256/640 [===========>..................] - ETA: 0s - loss: 1.4047 - accuracy: 0.0547
640/640 [==============================] - 0s 692us/sample - loss: 1.4107 - accuracy: 0.0516 - val_loss: 1.4226 - val_accuracy: 0.0437
Epoch 10/100
256/640 [===========>..................] - ETA: 0s - loss: 1.4017 - accuracy: 0.0547
640/640 [==============================] - 0s 745us/sample - loss: 1.4008 - accuracy: 0.0547 - val_loss: 1.4131 - val_accuracy: 0.0437
Epoch 11/100
256/640 [===========>..................] - ETA: 0s - loss: 1.3919 - accuracy: 0.0625
640/640 [==============================] - 0s 684us/sample - loss: 1.3910 - accuracy: 0.0562 - val_loss: 1.4038 - val_accuracy: 0.0437
Epoch 12/100
256/640 [===========>..................] - ETA: 0s - loss: 1.3849 - accuracy: 0.0625
640/640 [==============================] - 0s 765us/sample - loss: 1.3814 - accuracy: 0.0578 - val_loss: 1.3946 - val_accuracy: 0.0437
Epoch 13/100
256/640 [===========>..................] - ETA: 0s - loss: 1.3829 - accuracy: 0.0625
640/640 [==============================] - 0s 693us/sample - loss: 1.3720 - accuracy: 0.0672 - val_loss: 1.3856 - val_accuracy: 0.0500
Epoch 14/100
256/640 [===========>..................] - ETA: 0s - loss: 1.3533 - accuracy: 0.0859
640/640 [==============================] - 1s 855us/sample - loss: 1.3627 - accuracy: 0.0766 - val_loss: 1.3767 - val_accuracy: 0.0812
Epoch 15/100
256/640 [===========>..................] - ETA: 0s - loss: 1.3552 - accuracy: 0.0859
640/640 [==============================] - 0s 685us/sample - loss: 1.3535 - accuracy: 0.0875 - val_loss: 1.3678 - val_accuracy: 0.0875
Epoch 16/100
256/640 [===========>..................] - ETA: 0s - loss: 1.3397 - accuracy: 0.1094
640/640 [==============================] - 0s 722us/sample - loss: 1.3444 - accuracy: 0.0969 - val_loss: 1.3591 - val_accuracy: 0.1000
Epoch 17/100
256/640 [===========>..................] - ETA: 0s - loss: 1.3265 - accuracy: 0.1094
640/640 [==============================] - 0s 713us/sample - loss: 1.3355 - accuracy: 0.1047 - val_loss: 1.3505 - val_accuracy: 0.1063
Epoch 18/100
256/640 [===========>..................] - ETA: 0s - loss: 1.3294 - accuracy: 0.0938
640/640 [==============================] - 0s 722us/sample - loss: 1.3266 - accuracy: 0.1125 - val_loss: 1.3419 - val_accuracy: 0.1187
Epoch 19/100
256/640 [===========>..................] - ETA: 0s - loss: 1.3246 - accuracy: 0.0859
640/640 [==============================] - 0s 743us/sample - loss: 1.3178 - accuracy: 0.1234 - val_loss: 1.3335 - val_accuracy: 0.1187
Epoch 20/100
256/640 [===========>..................] - ETA: 0s - loss: 1.3168 - accuracy: 0.1211
640/640 [==============================] - 0s 698us/sample - loss: 1.3093 - accuracy: 0.1578 - val_loss: 1.3252 - val_accuracy: 0.1625
Epoch 21/100
256/640 [===========>..................] - ETA: 0s - loss: 1.3102 - accuracy: 0.1914
640/640 [==============================] - 1s 783us/sample - loss: 1.3008 - accuracy: 0.2500 - val_loss: 1.3169 - val_accuracy: 0.2562
Epoch 22/100
256/640 [===========>..................] - ETA: 0s - loss: 1.3015 - accuracy: 0.3438
640/640 [==============================] - 0s 718us/sample - loss: 1.2922 - accuracy: 0.3469 - val_loss: 1.3087 - val_accuracy: 0.3375
Epoch 23/100
256/640 [===========>..................] - ETA: 0s - loss: 1.3029 - accuracy: 0.3555
640/640 [==============================] - 0s 692us/sample - loss: 1.2840 - accuracy: 0.3844 - val_loss: 1.3006 - val_accuracy: 0.3625
Epoch 24/100
256/640 [===========>..................] - ETA: 0s - loss: 1.2744 - accuracy: 0.4141
640/640 [==============================] - 0s 692us/sample - loss: 1.2757 - accuracy: 0.4187 - val_loss: 1.2926 - val_accuracy: 0.3812
Epoch 25/100
256/640 [===========>..................] - ETA: 0s - loss: 1.2783 - accuracy: 0.4219
640/640 [==============================] - 0s 777us/sample - loss: 1.2674 - accuracy: 0.4313 - val_loss: 1.2846 - val_accuracy: 0.3812
Epoch 26/100
256/640 [===========>..................] - ETA: 0s - loss: 1.2626 - accuracy: 0.4492
640/640 [==============================] - 1s 927us/sample - loss: 1.2594 - accuracy: 0.4406 - val_loss: 1.2766 - val_accuracy: 0.3812
Epoch 27/100
256/640 [===========>..................] - ETA: 0s - loss: 1.2615 - accuracy: 0.4141
640/640 [==============================] - 1s 841us/sample - loss: 1.2510 - accuracy: 0.4469 - val_loss: 1.2689 - val_accuracy: 0.3875
Epoch 28/100
256/640 [===========>..................] - ETA: 0s - loss: 1.2365 - accuracy: 0.4766
640/640 [==============================] - 0s 709us/sample - loss: 1.2432 - accuracy: 0.4531 - val_loss: 1.2611 - val_accuracy: 0.4000
Epoch 29/100
256/640 [===========>..................] - ETA: 0s - loss: 1.2481 - accuracy: 0.4414
640/640 [==============================] - 1s 798us/sample - loss: 1.2351 - accuracy: 0.4594 - val_loss: 1.2533 - val_accuracy: 0.4062
Epoch 30/100
256/640 [===========>..................] - ETA: 0s - loss: 1.2273 - accuracy: 0.4609
640/640 [==============================] - 0s 741us/sample - loss: 1.2272 - accuracy: 0.4625 - val_loss: 1.2457 - val_accuracy: 0.4187
Epoch 31/100
256/640 [===========>..................] - ETA: 0s - loss: 1.2086 - accuracy: 0.4688
640/640 [==============================] - 0s 762us/sample - loss: 1.2194 - accuracy: 0.4672 - val_loss: 1.2380 - val_accuracy: 0.4187
Epoch 32/100
256/640 [===========>..................] - ETA: 0s - loss: 1.2002 - accuracy: 0.4883
640/640 [==============================] - 0s 731us/sample - loss: 1.2115 - accuracy: 0.4719 - val_loss: 1.2304 - val_accuracy: 0.4437
Epoch 33/100
256/640 [===========>..................] - ETA: 0s - loss: 1.2263 - accuracy: 0.4297
640/640 [==============================] - 0s 710us/sample - loss: 1.2035 - accuracy: 0.4797 - val_loss: 1.2229 - val_accuracy: 0.4563
Epoch 34/100
256/640 [===========>..................] - ETA: 0s - loss: 1.2016 - accuracy: 0.4922
640/640 [==============================] - 0s 779us/sample - loss: 1.1959 - accuracy: 0.4812 - val_loss: 1.2154 - val_accuracy: 0.4563
Epoch 35/100
256/640 [===========>..................] - ETA: 0s - loss: 1.1848 - accuracy: 0.4766
640/640 [==============================] - 0s 732us/sample - loss: 1.1883 - accuracy: 0.4859 - val_loss: 1.2079 - val_accuracy: 0.4625
Epoch 36/100
256/640 [===========>..................] - ETA: 0s - loss: 1.1577 - accuracy: 0.5039
640/640 [==============================] - 0s 725us/sample - loss: 1.1808 - accuracy: 0.4906 - val_loss: 1.2004 - val_accuracy: 0.4688
Epoch 37/100
256/640 [===========>..................] - ETA: 0s - loss: 1.1893 - accuracy: 0.4727
640/640 [==============================] - 0s 736us/sample - loss: 1.1730 - accuracy: 0.5000 - val_loss: 1.1931 - val_accuracy: 0.4688
Epoch 38/100
256/640 [===========>..................] - ETA: 0s - loss: 1.1555 - accuracy: 0.5234
640/640 [==============================] - 0s 710us/sample - loss: 1.1657 - accuracy: 0.5016 - val_loss: 1.1857 - val_accuracy: 0.4812
Epoch 39/100
256/640 [===========>..................] - ETA: 0s - loss: 1.1814 - accuracy: 0.4688
640/640 [==============================] - 0s 720us/sample - loss: 1.1581 - accuracy: 0.5094 - val_loss: 1.1785 - val_accuracy: 0.4875
Epoch 40/100
256/640 [===========>..................] - ETA: 0s - loss: 1.1639 - accuracy: 0.5039
640/640 [==============================] - 0s 685us/sample - loss: 1.1509 - accuracy: 0.5219 - val_loss: 1.1713 - val_accuracy: 0.5000
Epoch 41/100
256/640 [===========>..................] - ETA: 0s - loss: 1.1487 - accuracy: 0.5391
640/640 [==============================] - 0s 764us/sample - loss: 1.1436 - accuracy: 0.5328 - val_loss: 1.1643 - val_accuracy: 0.5063
Epoch 42/100
256/640 [===========>..................] - ETA: 0s - loss: 1.1172 - accuracy: 0.5547
640/640 [==============================] - 0s 726us/sample - loss: 1.1364 - accuracy: 0.5437 - val_loss: 1.1573 - val_accuracy: 0.5125
Epoch 43/100
256/640 [===========>..................] - ETA: 0s - loss: 1.1336 - accuracy: 0.5391
640/640 [==============================] - 0s 721us/sample - loss: 1.1293 - accuracy: 0.5453 - val_loss: 1.1504 - val_accuracy: 0.5125
Epoch 44/100
256/640 [===========>..................] - ETA: 0s - loss: 1.1080 - accuracy: 0.5508
640/640 [==============================] - 0s 674us/sample - loss: 1.1223 - accuracy: 0.5516 - val_loss: 1.1436 - val_accuracy: 0.5188
Epoch 45/100
256/640 [===========>..................] - ETA: 0s - loss: 1.1240 - accuracy: 0.5547
640/640 [==============================] - 1s 890us/sample - loss: 1.1153 - accuracy: 0.5625 - val_loss: 1.1368 - val_accuracy: 0.5375
Epoch 46/100
256/640 [===========>..................] - ETA: 0s - loss: 1.1000 - accuracy: 0.5625
640/640 [==============================] - 0s 705us/sample - loss: 1.1086 - accuracy: 0.5672 - val_loss: 1.1301 - val_accuracy: 0.5437
Epoch 47/100
256/640 [===========>..................] - ETA: 0s - loss: 1.1011 - accuracy: 0.6094
640/640 [==============================] - 0s 734us/sample - loss: 1.1018 - accuracy: 0.5813 - val_loss: 1.1235 - val_accuracy: 0.5500
Epoch 48/100
256/640 [===========>..................] - ETA: 0s - loss: 1.1340 - accuracy: 0.5312
640/640 [==============================] - 0s 731us/sample - loss: 1.0950 - accuracy: 0.5859 - val_loss: 1.1169 - val_accuracy: 0.5500
Epoch 49/100
256/640 [===========>..................] - ETA: 0s - loss: 1.0829 - accuracy: 0.6055
640/640 [==============================] - 0s 719us/sample - loss: 1.0885 - accuracy: 0.5922 - val_loss: 1.1104 - val_accuracy: 0.5500
Epoch 50/100
256/640 [===========>..................] - ETA: 0s - loss: 1.0939 - accuracy: 0.5742
640/640 [==============================] - 0s 736us/sample - loss: 1.0818 - accuracy: 0.5969 - val_loss: 1.1040 - val_accuracy: 0.5500
Epoch 51/100
256/640 [===========>..................] - ETA: 0s - loss: 1.0864 - accuracy: 0.6094
640/640 [==============================] - 0s 739us/sample - loss: 1.0754 - accuracy: 0.5984 - val_loss: 1.0977 - val_accuracy: 0.5562
Epoch 52/100
256/640 [===========>..................] - ETA: 0s - loss: 1.0536 - accuracy: 0.6211
640/640 [==============================] - 0s 755us/sample - loss: 1.0691 - accuracy: 0.5984 - val_loss: 1.0914 - val_accuracy: 0.5562
Epoch 53/100
256/640 [===========>..................] - ETA: 0s - loss: 1.0320 - accuracy: 0.6328
640/640 [==============================] - 0s 719us/sample - loss: 1.0628 - accuracy: 0.5984 - val_loss: 1.0853 - val_accuracy: 0.5562
Epoch 54/100
256/640 [===========>..................] - ETA: 0s - loss: 1.0316 - accuracy: 0.6055
640/640 [==============================] - 0s 740us/sample - loss: 1.0566 - accuracy: 0.5984 - val_loss: 1.0793 - val_accuracy: 0.5562
Epoch 55/100
256/640 [===========>..................] - ETA: 0s - loss: 1.0545 - accuracy: 0.5664
640/640 [==============================] - 0s 682us/sample - loss: 1.0504 - accuracy: 0.6000 - val_loss: 1.0734 - val_accuracy: 0.5562
Epoch 56/100
256/640 [===========>..................] - ETA: 0s - loss: 1.0161 - accuracy: 0.6250
640/640 [==============================] - 0s 732us/sample - loss: 1.0444 - accuracy: 0.6016 - val_loss: 1.0675 - val_accuracy: 0.5688
Epoch 57/100
256/640 [===========>..................] - ETA: 0s - loss: 1.0398 - accuracy: 0.6094
640/640 [==============================] - 0s 727us/sample - loss: 1.0385 - accuracy: 0.6031 - val_loss: 1.0618 - val_accuracy: 0.5688
Epoch 58/100
256/640 [===========>..................] - ETA: 0s - loss: 1.0844 - accuracy: 0.5977
640/640 [==============================] - 1s 830us/sample - loss: 1.0326 - accuracy: 0.6047 - val_loss: 1.0562 - val_accuracy: 0.5688
Epoch 59/100
256/640 [===========>..................] - ETA: 0s - loss: 1.0398 - accuracy: 0.5781
640/640 [==============================] - 0s 733us/sample - loss: 1.0269 - accuracy: 0.6047 - val_loss: 1.0507 - val_accuracy: 0.5688
Epoch 60/100
256/640 [===========>..................] - ETA: 0s - loss: 1.0314 - accuracy: 0.6016
640/640 [==============================] - 0s 726us/sample - loss: 1.0213 - accuracy: 0.6047 - val_loss: 1.0452 - val_accuracy: 0.5688
Epoch 61/100
256/640 [===========>..................] - ETA: 0s - loss: 1.0357 - accuracy: 0.5938
640/640 [==============================] - 0s 704us/sample - loss: 1.0156 - accuracy: 0.6062 - val_loss: 1.0398 - val_accuracy: 0.5750
Epoch 62/100
256/640 [===========>..................] - ETA: 0s - loss: 1.0012 - accuracy: 0.6094
640/640 [==============================] - 0s 706us/sample - loss: 1.0102 - accuracy: 0.6062 - val_loss: 1.0344 - val_accuracy: 0.5813
Epoch 63/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9986 - accuracy: 0.6445
640/640 [==============================] - 0s 727us/sample - loss: 1.0048 - accuracy: 0.6047 - val_loss: 1.0292 - val_accuracy: 0.5813
Epoch 64/100
256/640 [===========>..................] - ETA: 0s - loss: 1.0274 - accuracy: 0.5898
640/640 [==============================] - 0s 696us/sample - loss: 0.9993 - accuracy: 0.6062 - val_loss: 1.0241 - val_accuracy: 0.5813
Epoch 65/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9807 - accuracy: 0.6211
640/640 [==============================] - 0s 726us/sample - loss: 0.9941 - accuracy: 0.6062 - val_loss: 1.0190 - val_accuracy: 0.5813
Epoch 66/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9610 - accuracy: 0.6289
640/640 [==============================] - 0s 725us/sample - loss: 0.9890 - accuracy: 0.6094 - val_loss: 1.0140 - val_accuracy: 0.5813
Epoch 67/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9809 - accuracy: 0.6094
640/640 [==============================] - 0s 740us/sample - loss: 0.9838 - accuracy: 0.6094 - val_loss: 1.0091 - val_accuracy: 0.5813
Epoch 68/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9677 - accuracy: 0.5977
640/640 [==============================] - 0s 756us/sample - loss: 0.9788 - accuracy: 0.6094 - val_loss: 1.0043 - val_accuracy: 0.5813
Epoch 69/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9717 - accuracy: 0.5742
640/640 [==============================] - 0s 716us/sample - loss: 0.9740 - accuracy: 0.6094 - val_loss: 0.9996 - val_accuracy: 0.5813
Epoch 70/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9692 - accuracy: 0.6250
640/640 [==============================] - 0s 765us/sample - loss: 0.9691 - accuracy: 0.6125 - val_loss: 0.9949 - val_accuracy: 0.5875
Epoch 71/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9481 - accuracy: 0.6016
640/640 [==============================] - 0s 698us/sample - loss: 0.9645 - accuracy: 0.6125 - val_loss: 0.9903 - val_accuracy: 0.5875
Epoch 72/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9825 - accuracy: 0.6016
640/640 [==============================] - 0s 738us/sample - loss: 0.9596 - accuracy: 0.6109 - val_loss: 0.9859 - val_accuracy: 0.5875
Epoch 73/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9662 - accuracy: 0.5820
640/640 [==============================] - 0s 730us/sample - loss: 0.9551 - accuracy: 0.6125 - val_loss: 0.9815 - val_accuracy: 0.5875
Epoch 74/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9719 - accuracy: 0.6133
640/640 [==============================] - 0s 716us/sample - loss: 0.9506 - accuracy: 0.6156 - val_loss: 0.9772 - val_accuracy: 0.5875
Epoch 75/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9291 - accuracy: 0.6250
640/640 [==============================] - 0s 723us/sample - loss: 0.9462 - accuracy: 0.6141 - val_loss: 0.9729 - val_accuracy: 0.5875
Epoch 76/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9314 - accuracy: 0.6133
640/640 [==============================] - 0s 713us/sample - loss: 0.9418 - accuracy: 0.6141 - val_loss: 0.9687 - val_accuracy: 0.5875
Epoch 77/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9086 - accuracy: 0.6211
640/640 [==============================] - 1s 922us/sample - loss: 0.9374 - accuracy: 0.6141 - val_loss: 0.9646 - val_accuracy: 0.5875
Epoch 78/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9106 - accuracy: 0.6055
640/640 [==============================] - 0s 701us/sample - loss: 0.9333 - accuracy: 0.6141 - val_loss: 0.9605 - val_accuracy: 0.5875
Epoch 79/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9079 - accuracy: 0.6641
640/640 [==============================] - 1s 809us/sample - loss: 0.9290 - accuracy: 0.6141 - val_loss: 0.9564 - val_accuracy: 0.5938
Epoch 80/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9537 - accuracy: 0.6016
640/640 [==============================] - 1s 935us/sample - loss: 0.9248 - accuracy: 0.6141 - val_loss: 0.9524 - val_accuracy: 0.5938
Epoch 81/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9047 - accuracy: 0.6602
640/640 [==============================] - 0s 724us/sample - loss: 0.9208 - accuracy: 0.6156 - val_loss: 0.9485 - val_accuracy: 0.5938
Epoch 82/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9510 - accuracy: 0.6055
640/640 [==============================] - 0s 682us/sample - loss: 0.9168 - accuracy: 0.6156 - val_loss: 0.9447 - val_accuracy: 0.5938
Epoch 83/100
256/640 [===========>..................] - ETA: 0s - loss: 0.8958 - accuracy: 0.6562
640/640 [==============================] - 0s 720us/sample - loss: 0.9129 - accuracy: 0.6172 - val_loss: 0.9409 - val_accuracy: 0.5938
Epoch 84/100
256/640 [===========>..................] - ETA: 0s - loss: 0.8977 - accuracy: 0.6484
640/640 [==============================] - 1s 796us/sample - loss: 0.9089 - accuracy: 0.6172 - val_loss: 0.9372 - val_accuracy: 0.5938
Epoch 85/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9219 - accuracy: 0.6680
640/640 [==============================] - 0s 726us/sample - loss: 0.9052 - accuracy: 0.6203 - val_loss: 0.9335 - val_accuracy: 0.5938
Epoch 86/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9469 - accuracy: 0.6055
640/640 [==============================] - 0s 697us/sample - loss: 0.9014 - accuracy: 0.6187 - val_loss: 0.9298 - val_accuracy: 0.5938
Epoch 87/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9356 - accuracy: 0.5781
640/640 [==============================] - 0s 698us/sample - loss: 0.8978 - accuracy: 0.6187 - val_loss: 0.9262 - val_accuracy: 0.5938
Epoch 88/100
256/640 [===========>..................] - ETA: 0s - loss: 0.8746 - accuracy: 0.6055
640/640 [==============================] - 0s 707us/sample - loss: 0.8941 - accuracy: 0.6203 - val_loss: 0.9227 - val_accuracy: 0.5938
Epoch 89/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9180 - accuracy: 0.5898
640/640 [==============================] - 0s 698us/sample - loss: 0.8904 - accuracy: 0.6219 - val_loss: 0.9192 - val_accuracy: 0.6000
Epoch 90/100
256/640 [===========>..................] - ETA: 0s - loss: 0.8682 - accuracy: 0.6250
640/640 [==============================] - 1s 867us/sample - loss: 0.8869 - accuracy: 0.6219 - val_loss: 0.9158 - val_accuracy: 0.6000
Epoch 91/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9374 - accuracy: 0.5625
640/640 [==============================] - 0s 732us/sample - loss: 0.8834 - accuracy: 0.6219 - val_loss: 0.9124 - val_accuracy: 0.6000
Epoch 92/100
256/640 [===========>..................] - ETA: 0s - loss: 0.8757 - accuracy: 0.6250
640/640 [==============================] - 1s 900us/sample - loss: 0.8800 - accuracy: 0.6219 - val_loss: 0.9091 - val_accuracy: 0.6000
Epoch 93/100
256/640 [===========>..................] - ETA: 0s - loss: 0.8783 - accuracy: 0.6172
640/640 [==============================] - 0s 705us/sample - loss: 0.8766 - accuracy: 0.6203 - val_loss: 0.9058 - val_accuracy: 0.5938
Epoch 94/100
256/640 [===========>..................] - ETA: 0s - loss: 0.8668 - accuracy: 0.6406
640/640 [==============================] - 0s 740us/sample - loss: 0.8732 - accuracy: 0.6187 - val_loss: 0.9026 - val_accuracy: 0.5938
Epoch 95/100
256/640 [===========>..................] - ETA: 0s - loss: 0.8138 - accuracy: 0.6445
640/640 [==============================] - 0s 739us/sample - loss: 0.8699 - accuracy: 0.6187 - val_loss: 0.8995 - val_accuracy: 0.5938
Epoch 96/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9218 - accuracy: 0.5977
640/640 [==============================] - 0s 740us/sample - loss: 0.8666 - accuracy: 0.6187 - val_loss: 0.8964 - val_accuracy: 0.5938
Epoch 97/100
256/640 [===========>..................] - ETA: 0s - loss: 0.8564 - accuracy: 0.6133
640/640 [==============================] - 0s 717us/sample - loss: 0.8635 - accuracy: 0.6203 - val_loss: 0.8934 - val_accuracy: 0.5938
Epoch 98/100
256/640 [===========>..................] - ETA: 0s - loss: 0.9030 - accuracy: 0.6094
640/640 [==============================] - 0s 730us/sample - loss: 0.8602 - accuracy: 0.6203 - val_loss: 0.8904 - val_accuracy: 0.5938
Epoch 99/100
256/640 [===========>..................] - ETA: 0s - loss: 0.8412 - accuracy: 0.6562
640/640 [==============================] - 1s 1ms/sample - loss: 0.8573 - accuracy: 0.6203 - val_loss: 0.8875 - val_accuracy: 0.5938
Epoch 100/100
256/640 [===========>..................] - ETA: 0s - loss: 0.8629 - accuracy: 0.6211
640/640 [==============================] - 0s 753us/sample - loss: 0.8541 - accuracy: 0.6203 - val_loss: 0.8846 - val_accuracy: 0.5938
Time taken to train model: 48.6715362071991
200/1 [================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 0s 38us/sample - loss: 0.8867 - accuracy: 0.6300
Confusion Matrix and Statistics
Reference
Prediction 0 1 2 3
0 56 0 1 0
1 2 6 3 42
2 6 3 32 1
3 8 2 6 32
Overall Statistics
Accuracy : 0.63
95% CI : (0.5591, 0.697)
No Information Rate : 0.375
P-Value [Acc > NIR] : 2.568e-13
Kappa : 0.5058
Mcnemar's Test P-Value : 9.276e-10
Statistics by Class:
Class: 0 Class: 1 Class: 2
Sensitivity 0.7778 0.5455 0.7619
Specificity 0.9922 0.7513 0.9367
Pos Pred Value 0.9825 0.1132 0.7619
Neg Pred Value 0.8881 0.9660 0.9367
Prevalence 0.3600 0.0550 0.2100
Detection Rate 0.2800 0.0300 0.1600
Detection Prevalence 0.2850 0.2650 0.2100
Balanced Accuracy 0.8850 0.6484 0.8493
Class: 3
Sensitivity 0.4267
Specificity 0.8720
Pos Pred Value 0.6667
Neg Pred Value 0.7171
Prevalence 0.3750
Detection Rate 0.1600
Detection Prevalence 0.2400
Balanced Accuracy 0.6493Table for Time it took to train the network with different batch size
Batch_Size <- c(1,32,64,128,264)
Time_Sec <- c(192,54.02,52.79,51.84,51.21)
time_table <- data.frame(Batch_Size,Time_Sec)
colnames(time_table) <- c("Batch Size","Time (in seconds)")
cat(paste0("Time it took to train the network with different batch size is as follows:"))Time it took to train the network with different batch size is as follows:print(time_table)
#With increase in batch size, time taken to train network reduces.Part 2.2 (c)
acc <- c(0.81,0.78,0.775,0.695,0.64)
class0 <- c(0.9436,0.9436,0.9338,0.9142,0.9020)
class1 <- c(0.8339,0.8164,0.79,0.7545,0.6445)
class2 <- c(0.8690,0.8356,0.9001,0.7691,0.8495)
class3 <- c(0.8638,0.8235,0.7563,0.7314,0.6338)
new_table <- data.frame(Batch_Size,acc,class0,class1,class2,class3)
colnames(new_table) <- c("Batch Size","Overall Accuracy","Balanced Accuracy for class0","Balanced Accuracy for class1","Balanced Accuracy for class2","Balanced Accuracy for class3")
print(new_table)
#Overall accuracy decreases with increase in batch size.
#Balanced accuracy in class 0,1,3 decreases with increase in batch size.Part 2.2 (d)
model <- keras_model_sequential() %>% layer_dense(units = 10, activation="relu", input_shape=c(3)) %>% layer_dense(units =5, activation="relu") %>% layer_dense(units = 4, activation="softmax")
model %>%
compile(loss = "categorical_crossentropy", optimizer="adam", metrics=c("accuracy"))
model %>% fit(
data.matrix(X_train),
y_train.ohe,
epochs=100,
batch_size=1,
validation_split=0.20
)Train on 640 samples, validate on 160 samples
Epoch 1/100
1/640 [..............................] - ETA: 3:43 - loss: 0.8364 - accuracy: 0.0000e+00
30/640 [>.............................] - ETA: 8s - loss: 1.3808 - accuracy: 0.0333
56/640 [=>............................] - ETA: 4s - loss: 1.3768 - accuracy: 0.1964
87/640 [===>..........................] - ETA: 3s - loss: 1.3418 - accuracy: 0.2989
120/640 [====>.........................] - ETA: 2s - loss: 1.3310 - accuracy: 0.3250
154/640 [======>.......................] - ETA: 1s - loss: 1.2906 - accuracy: 0.3766
187/640 [=======>......................] - ETA: 1s - loss: 1.2637 - accuracy: 0.4385
218/640 [=========>....................] - ETA: 1s - loss: 1.2641 - accuracy: 0.4679
247/640 [==========>...................] - ETA: 1s - loss: 1.2445 - accuracy: 0.4899
274/640 [===========>..................] - ETA: 1s - loss: 1.2383 - accuracy: 0.4964
302/640 [=============>................] - ETA: 0s - loss: 1.2259 - accuracy: 0.5000
328/640 [==============>...............] - ETA: 0s - loss: 1.2107 - accuracy: 0.5213
355/640 [===============>..............] - ETA: 0s - loss: 1.2060 - accuracy: 0.5211
381/640 [================>.............] - ETA: 0s - loss: 1.1908 - accuracy: 0.5381
413/640 [==================>...........] - ETA: 0s - loss: 1.1813 - accuracy: 0.5400
447/640 [===================>..........] - ETA: 0s - loss: 1.1757 - accuracy: 0.5369
480/640 [=====================>........] - ETA: 0s - loss: 1.1627 - accuracy: 0.5437
513/640 [=======================>......] - ETA: 0s - loss: 1.1552 - accuracy: 0.5439
547/640 [========================>.....] - ETA: 0s - loss: 1.1517 - accuracy: 0.5411
580/640 [==========================>...] - ETA: 0s - loss: 1.1417 - accuracy: 0.5517
600/640 [===========================>..] - ETA: 0s - loss: 1.1313 - accuracy: 0.5567
621/640 [============================>.] - ETA: 0s - loss: 1.1243 - accuracy: 0.5588
640/640 [==============================] - 2s 3ms/sample - loss: 1.1179 - accuracy: 0.5594 - val_loss: 0.9481 - val_accuracy: 0.6375
Epoch 2/100
1/640 [..............................] - ETA: 2s - loss: 1.2571 - accuracy: 0.0000e+00
25/640 [>.............................] - ETA: 1s - loss: 0.9425 - accuracy: 0.6400
53/640 [=>............................] - ETA: 1s - loss: 0.9150 - accuracy: 0.6792
72/640 [==>...........................] - ETA: 1s - loss: 0.9468 - accuracy: 0.6389
99/640 [===>..........................] - ETA: 1s - loss: 0.9178 - accuracy: 0.6566
130/640 [=====>........................] - ETA: 1s - loss: 0.9254 - accuracy: 0.6231
140/640 [=====>........................] - ETA: 1s - loss: 0.9041 - accuracy: 0.6429
157/640 [======>.......................] - ETA: 1s - loss: 0.9172 - accuracy: 0.6561
177/640 [=======>......................] - ETA: 1s - loss: 0.8986 - accuracy: 0.6836
195/640 [========>.....................] - ETA: 1s - loss: 0.8919 - accuracy: 0.6923
213/640 [========>.....................] - ETA: 1s - loss: 0.9030 - accuracy: 0.6901
229/640 [=========>....................] - ETA: 1s - loss: 0.9102 - accuracy: 0.6681
255/640 [==========>...................] - ETA: 0s - loss: 0.9009 - accuracy: 0.6588
286/640 [============>.................] - ETA: 0s - loss: 0.8951 - accuracy: 0.6608
315/640 [=============>................] - ETA: 0s - loss: 0.8950 - accuracy: 0.6571
345/640 [===============>..............] - ETA: 0s - loss: 0.8836 - accuracy: 0.6522
376/640 [================>.............] - ETA: 0s - loss: 0.8753 - accuracy: 0.6516
405/640 [=================>............] - ETA: 0s - loss: 0.8734 - accuracy: 0.6568
434/640 [===================>..........] - ETA: 0s - loss: 0.8719 - accuracy: 0.6590
467/640 [====================>.........] - ETA: 0s - loss: 0.8637 - accuracy: 0.6595
496/640 [======================>.......] - ETA: 0s - loss: 0.8571 - accuracy: 0.6593
512/640 [=======================>......] - ETA: 0s - loss: 0.8566 - accuracy: 0.6621
529/640 [=======================>......] - ETA: 0s - loss: 0.8572 - accuracy: 0.6597
553/640 [========================>.....] - ETA: 0s - loss: 0.8519 - accuracy: 0.6546
570/640 [=========================>....] - ETA: 0s - loss: 0.8583 - accuracy: 0.6526
597/640 [==========================>...] - ETA: 0s - loss: 0.8576 - accuracy: 0.6533
621/640 [============================>.] - ETA: 0s - loss: 0.8570 - accuracy: 0.6570
640/640 [==============================] - 2s 3ms/sample - loss: 0.8551 - accuracy: 0.6531 - val_loss: 0.7786 - val_accuracy: 0.6625
Epoch 3/100
1/640 [..............................] - ETA: 5s - loss: 0.8085 - accuracy: 1.0000
26/640 [>.............................] - ETA: 1s - loss: 0.7402 - accuracy: 0.6154
55/640 [=>............................] - ETA: 1s - loss: 0.7600 - accuracy: 0.5818
84/640 [==>...........................] - ETA: 1s - loss: 0.7574 - accuracy: 0.6071
113/640 [====>.........................] - ETA: 1s - loss: 0.7406 - accuracy: 0.6460
141/640 [=====>........................] - ETA: 0s - loss: 0.7514 - accuracy: 0.6312
167/640 [======>.......................] - ETA: 0s - loss: 0.7497 - accuracy: 0.6287
189/640 [=======>......................] - ETA: 0s - loss: 0.7434 - accuracy: 0.6455
207/640 [========>.....................] - ETA: 0s - loss: 0.7464 - accuracy: 0.6522
219/640 [=========>....................] - ETA: 0s - loss: 0.7506 - accuracy: 0.6530
242/640 [==========>...................] - ETA: 0s - loss: 0.7508 - accuracy: 0.6570
274/640 [===========>..................] - ETA: 0s - loss: 0.7445 - accuracy: 0.6569
307/640 [=============>................] - ETA: 0s - loss: 0.7441 - accuracy: 0.6580
340/640 [==============>...............] - ETA: 0s - loss: 0.7313 - accuracy: 0.6676
375/640 [================>.............] - ETA: 0s - loss: 0.7180 - accuracy: 0.6773
408/640 [==================>...........] - ETA: 0s - loss: 0.7265 - accuracy: 0.6765
441/640 [===================>..........] - ETA: 0s - loss: 0.7398 - accuracy: 0.6735
474/640 [=====================>........] - ETA: 0s - loss: 0.7299 - accuracy: 0.6751
501/640 [======================>.......] - ETA: 0s - loss: 0.7290 - accuracy: 0.6786
528/640 [=======================>......] - ETA: 0s - loss: 0.7385 - accuracy: 0.6856
558/640 [=========================>....] - ETA: 0s - loss: 0.7387 - accuracy: 0.6846
587/640 [==========================>...] - ETA: 0s - loss: 0.7336 - accuracy: 0.6882
615/640 [===========================>..] - ETA: 0s - loss: 0.7297 - accuracy: 0.6878
640/640 [==============================] - 2s 3ms/sample - loss: 0.7260 - accuracy: 0.6875 - val_loss: 0.6998 - val_accuracy: 0.7063
Epoch 4/100
1/640 [..............................] - ETA: 5s - loss: 0.9229 - accuracy: 1.0000
22/640 [>.............................] - ETA: 1s - loss: 0.5182 - accuracy: 0.8636
41/640 [>.............................] - ETA: 1s - loss: 0.5516 - accuracy: 0.7805
64/640 [==>...........................] - ETA: 1s - loss: 0.6400 - accuracy: 0.7188
91/640 [===>..........................] - ETA: 1s - loss: 0.6618 - accuracy: 0.7253
122/640 [====>.........................] - ETA: 1s - loss: 0.6837 - accuracy: 0.7131
149/640 [=====>........................] - ETA: 1s - loss: 0.6311 - accuracy: 0.7450
177/640 [=======>......................] - ETA: 0s - loss: 0.6203 - accuracy: 0.7458
203/640 [========>.....................] - ETA: 0s - loss: 0.6529 - accuracy: 0.7241
228/640 [=========>....................] - ETA: 0s - loss: 0.6547 - accuracy: 0.7105
255/640 [==========>...................] - ETA: 0s - loss: 0.6541 - accuracy: 0.7098
287/640 [============>.................] - ETA: 0s - loss: 0.6605 - accuracy: 0.7038
319/640 [=============>................] - ETA: 0s - loss: 0.6860 - accuracy: 0.7147
343/640 [===============>..............] - ETA: 0s - loss: 0.6744 - accuracy: 0.7172
359/640 [===============>..............] - ETA: 0s - loss: 0.6683 - accuracy: 0.7214
385/640 [=================>............] - ETA: 0s - loss: 0.6667 - accuracy: 0.7299
416/640 [==================>...........] - ETA: 0s - loss: 0.6582 - accuracy: 0.7356
447/640 [===================>..........] - ETA: 0s - loss: 0.6691 - accuracy: 0.7383
480/640 [=====================>........] - ETA: 0s - loss: 0.6837 - accuracy: 0.7354
514/640 [=======================>......] - ETA: 0s - loss: 0.6818 - accuracy: 0.7315
546/640 [========================>.....] - ETA: 0s - loss: 0.6718 - accuracy: 0.7363
576/640 [==========================>...] - ETA: 0s - loss: 0.6673 - accuracy: 0.7326
601/640 [===========================>..] - ETA: 0s - loss: 0.6659 - accuracy: 0.7371
630/640 [============================>.] - ETA: 0s - loss: 0.6687 - accuracy: 0.7349
640/640 [==============================] - 2s 3ms/sample - loss: 0.6670 - accuracy: 0.7344 - val_loss: 0.6596 - val_accuracy: 0.7250
Epoch 5/100
1/640 [..............................] - ETA: 1s - loss: 0.2269 - accuracy: 1.0000
28/640 [>.............................] - ETA: 1s - loss: 0.5560 - accuracy: 0.8214
50/640 [=>............................] - ETA: 1s - loss: 0.6574 - accuracy: 0.7200
66/640 [==>...........................] - ETA: 1s - loss: 0.6526 - accuracy: 0.7273
96/640 [===>..........................] - ETA: 1s - loss: 0.6319 - accuracy: 0.7500
125/640 [====>.........................] - ETA: 1s - loss: 0.6630 - accuracy: 0.7360
152/640 [======>.......................] - ETA: 1s - loss: 0.6556 - accuracy: 0.7500
183/640 [=======>......................] - ETA: 0s - loss: 0.6680 - accuracy: 0.7268
211/640 [========>.....................] - ETA: 0s - loss: 0.6380 - accuracy: 0.7441
244/640 [==========>...................] - ETA: 0s - loss: 0.6529 - accuracy: 0.7377
267/640 [===========>..................] - ETA: 0s - loss: 0.6430 - accuracy: 0.7378
284/640 [============>.................] - ETA: 0s - loss: 0.6332 - accuracy: 0.7465
310/640 [=============>................] - ETA: 0s - loss: 0.6334 - accuracy: 0.7355
334/640 [==============>...............] - ETA: 0s - loss: 0.6420 - accuracy: 0.7335
363/640 [================>.............] - ETA: 0s - loss: 0.6603 - accuracy: 0.7273
397/640 [=================>............] - ETA: 0s - loss: 0.6504 - accuracy: 0.7305
430/640 [===================>..........] - ETA: 0s - loss: 0.6437 - accuracy: 0.7372
462/640 [====================>.........] - ETA: 0s - loss: 0.6443 - accuracy: 0.7359
494/640 [======================>.......] - ETA: 0s - loss: 0.6473 - accuracy: 0.7409
510/640 [======================>.......] - ETA: 0s - loss: 0.6495 - accuracy: 0.7412
538/640 [========================>.....] - ETA: 0s - loss: 0.6465 - accuracy: 0.7416
567/640 [=========================>....] - ETA: 0s - loss: 0.6361 - accuracy: 0.7496
600/640 [===========================>..] - ETA: 0s - loss: 0.6280 - accuracy: 0.7550
632/640 [============================>.] - ETA: 0s - loss: 0.6335 - accuracy: 0.7563
640/640 [==============================] - 2s 3ms/sample - loss: 0.6342 - accuracy: 0.7531 - val_loss: 0.6385 - val_accuracy: 0.7500
Epoch 6/100
1/640 [..............................] - ETA: 5s - loss: 0.0988 - accuracy: 1.0000
13/640 [..............................] - ETA: 2s - loss: 0.4212 - accuracy: 0.7692
32/640 [>.............................] - ETA: 2s - loss: 0.5360 - accuracy: 0.7188
55/640 [=>............................] - ETA: 1s - loss: 0.5885 - accuracy: 0.6909
74/640 [==>...........................] - ETA: 1s - loss: 0.6046 - accuracy: 0.7162
102/640 [===>..........................] - ETA: 1s - loss: 0.6149 - accuracy: 0.7451
128/640 [=====>........................] - ETA: 1s - loss: 0.6413 - accuracy: 0.7266
160/640 [======>.......................] - ETA: 1s - loss: 0.6353 - accuracy: 0.7188
188/640 [=======>......................] - ETA: 1s - loss: 0.6261 - accuracy: 0.7234
219/640 [=========>....................] - ETA: 0s - loss: 0.6441 - accuracy: 0.7306
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640/640 [==============================] - 2s 3ms/sample - loss: 0.6120 - accuracy: 0.7531 - val_loss: 0.6209 - val_accuracy: 0.7563
Epoch 7/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5958 - accuracy: 0.7703 - val_loss: 0.6108 - val_accuracy: 0.7000
Epoch 8/100
1/640 [..............................] - ETA: 2s - loss: 0.7380 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5865 - accuracy: 0.7641 - val_loss: 0.6010 - val_accuracy: 0.7500
Epoch 9/100
1/640 [..............................] - ETA: 4s - loss: 0.0147 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5756 - accuracy: 0.7641 - val_loss: 0.5928 - val_accuracy: 0.7563
Epoch 10/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5677 - accuracy: 0.7750 - val_loss: 0.5788 - val_accuracy: 0.7437
Epoch 11/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5660 - accuracy: 0.7703 - val_loss: 0.5726 - val_accuracy: 0.7625
Epoch 12/100
1/640 [..............................] - ETA: 5s - loss: 0.8582 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5585 - accuracy: 0.7812 - val_loss: 0.5734 - val_accuracy: 0.7688
Epoch 13/100
1/640 [..............................] - ETA: 1s - loss: 0.0022 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5550 - accuracy: 0.7734 - val_loss: 0.5684 - val_accuracy: 0.7500
Epoch 14/100
1/640 [..............................] - ETA: 1s - loss: 0.0083 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5523 - accuracy: 0.7719 - val_loss: 0.5700 - val_accuracy: 0.7500
Epoch 15/100
1/640 [..............................] - ETA: 6s - loss: 0.0216 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5453 - accuracy: 0.7812 - val_loss: 0.5655 - val_accuracy: 0.7500
Epoch 16/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5428 - accuracy: 0.7812 - val_loss: 0.5668 - val_accuracy: 0.7563
Epoch 17/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5371 - accuracy: 0.7891 - val_loss: 0.5595 - val_accuracy: 0.7437
Epoch 18/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5366 - accuracy: 0.7812 - val_loss: 0.5545 - val_accuracy: 0.7500
Epoch 19/100
1/640 [..............................] - ETA: 5s - loss: 0.0283 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5332 - accuracy: 0.8031 - val_loss: 0.5518 - val_accuracy: 0.7375
Epoch 20/100
1/640 [..............................] - ETA: 5s - loss: 3.5763e-06 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5324 - accuracy: 0.7859 - val_loss: 0.5513 - val_accuracy: 0.7437
Epoch 21/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5289 - accuracy: 0.7750 - val_loss: 0.5511 - val_accuracy: 0.7500
Epoch 22/100
1/640 [..............................] - ETA: 6s - loss: 1.1910 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5281 - accuracy: 0.7891 - val_loss: 0.5469 - val_accuracy: 0.7563
Epoch 23/100
1/640 [..............................] - ETA: 5s - loss: 0.3786 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5245 - accuracy: 0.7875 - val_loss: 0.5474 - val_accuracy: 0.7500
Epoch 24/100
1/640 [..............................] - ETA: 4s - loss: 0.0549 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5206 - accuracy: 0.7891 - val_loss: 0.5393 - val_accuracy: 0.7625
Epoch 25/100
1/640 [..............................] - ETA: 5s - loss: 0.6485 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5184 - accuracy: 0.7984 - val_loss: 0.5378 - val_accuracy: 0.7312
Epoch 26/100
1/640 [..............................] - ETA: 6s - loss: 0.0739 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5165 - accuracy: 0.7906 - val_loss: 0.5380 - val_accuracy: 0.7312
Epoch 27/100
1/640 [..............................] - ETA: 2s - loss: 0.1012 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5113 - accuracy: 0.7969 - val_loss: 0.5381 - val_accuracy: 0.7625
Epoch 28/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5112 - accuracy: 0.7922 - val_loss: 0.5346 - val_accuracy: 0.7500
Epoch 29/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5052 - accuracy: 0.8031 - val_loss: 0.5353 - val_accuracy: 0.7500
Epoch 30/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5031 - accuracy: 0.8031 - val_loss: 0.5373 - val_accuracy: 0.7437
Epoch 31/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5049 - accuracy: 0.8000 - val_loss: 0.5353 - val_accuracy: 0.7625
Epoch 32/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5018 - accuracy: 0.8016 - val_loss: 0.5270 - val_accuracy: 0.7375
Epoch 33/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.5001 - accuracy: 0.8062 - val_loss: 0.5271 - val_accuracy: 0.7563
Epoch 34/100
1/640 [..............................] - ETA: 0s - loss: 0.4379 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4965 - accuracy: 0.8047 - val_loss: 0.5248 - val_accuracy: 0.7375
Epoch 35/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4955 - accuracy: 0.8094 - val_loss: 0.5199 - val_accuracy: 0.7437
Epoch 36/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4892 - accuracy: 0.8062 - val_loss: 0.5247 - val_accuracy: 0.7437
Epoch 37/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4917 - accuracy: 0.8031 - val_loss: 0.5242 - val_accuracy: 0.7500
Epoch 38/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4893 - accuracy: 0.8156 - val_loss: 0.5223 - val_accuracy: 0.7312
Epoch 39/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4899 - accuracy: 0.8109 - val_loss: 0.5201 - val_accuracy: 0.7437
Epoch 40/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4887 - accuracy: 0.8047 - val_loss: 0.5149 - val_accuracy: 0.7437
Epoch 41/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4881 - accuracy: 0.8094 - val_loss: 0.5164 - val_accuracy: 0.7437
Epoch 42/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4846 - accuracy: 0.8078 - val_loss: 0.5205 - val_accuracy: 0.7750
Epoch 43/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4838 - accuracy: 0.8078 - val_loss: 0.5083 - val_accuracy: 0.7437
Epoch 44/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4816 - accuracy: 0.8219 - val_loss: 0.5104 - val_accuracy: 0.7812
Epoch 45/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4819 - accuracy: 0.8141 - val_loss: 0.5103 - val_accuracy: 0.7875
Epoch 46/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4843 - accuracy: 0.8203 - val_loss: 0.5165 - val_accuracy: 0.7750
Epoch 47/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4824 - accuracy: 0.8156 - val_loss: 0.5130 - val_accuracy: 0.7688
Epoch 48/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4772 - accuracy: 0.8156 - val_loss: 0.5357 - val_accuracy: 0.7437
Epoch 49/100
1/640 [..............................] - ETA: 3s - loss: 0.0000e+00 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4815 - accuracy: 0.8109 - val_loss: 0.5068 - val_accuracy: 0.7625
Epoch 50/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4805 - accuracy: 0.8125 - val_loss: 0.5010 - val_accuracy: 0.7437
Epoch 51/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4796 - accuracy: 0.8203 - val_loss: 0.5061 - val_accuracy: 0.7437
Epoch 52/100
1/640 [..............................] - ETA: 1s - loss: 0.0000e+00 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4770 - accuracy: 0.8188 - val_loss: 0.5100 - val_accuracy: 0.7375
Epoch 53/100
1/640 [..............................] - ETA: 4s - loss: 1.1672 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4788 - accuracy: 0.8125 - val_loss: 0.5030 - val_accuracy: 0.7625
Epoch 54/100
1/640 [..............................] - ETA: 6s - loss: 0.5337 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4761 - accuracy: 0.8266 - val_loss: 0.5086 - val_accuracy: 0.7688
Epoch 55/100
1/640 [..............................] - ETA: 5s - loss: 0.3322 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4757 - accuracy: 0.8203 - val_loss: 0.5055 - val_accuracy: 0.7688
Epoch 56/100
1/640 [..............................] - ETA: 3s - loss: 0.4369 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4735 - accuracy: 0.8172 - val_loss: 0.5355 - val_accuracy: 0.7500
Epoch 57/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4780 - accuracy: 0.8141 - val_loss: 0.5028 - val_accuracy: 0.7500
Epoch 58/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4747 - accuracy: 0.8188 - val_loss: 0.5082 - val_accuracy: 0.7812
Epoch 59/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4759 - accuracy: 0.8250 - val_loss: 0.5018 - val_accuracy: 0.7500
Epoch 60/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4748 - accuracy: 0.8203 - val_loss: 0.5052 - val_accuracy: 0.7563
Epoch 61/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4741 - accuracy: 0.8109 - val_loss: 0.5053 - val_accuracy: 0.7563
Epoch 62/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4736 - accuracy: 0.8156 - val_loss: 0.4994 - val_accuracy: 0.7563
Epoch 63/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4720 - accuracy: 0.8188 - val_loss: 0.4980 - val_accuracy: 0.7688
Epoch 64/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4741 - accuracy: 0.8234 - val_loss: 0.5007 - val_accuracy: 0.7625
Epoch 65/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4721 - accuracy: 0.8188 - val_loss: 0.5044 - val_accuracy: 0.7750
Epoch 66/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4721 - accuracy: 0.8203 - val_loss: 0.4996 - val_accuracy: 0.7812
Epoch 67/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4718 - accuracy: 0.8219 - val_loss: 0.5014 - val_accuracy: 0.7750
Epoch 68/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4708 - accuracy: 0.8172 - val_loss: 0.5043 - val_accuracy: 0.7875
Epoch 69/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4731 - accuracy: 0.8203 - val_loss: 0.4941 - val_accuracy: 0.7812
Epoch 70/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4709 - accuracy: 0.8172 - val_loss: 0.4973 - val_accuracy: 0.7688
Epoch 71/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4662 - accuracy: 0.8188 - val_loss: 0.5050 - val_accuracy: 0.7688
Epoch 72/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4703 - accuracy: 0.8219 - val_loss: 0.4965 - val_accuracy: 0.7625
Epoch 73/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4712 - accuracy: 0.8188 - val_loss: 0.4968 - val_accuracy: 0.7625
Epoch 74/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4702 - accuracy: 0.8219 - val_loss: 0.4985 - val_accuracy: 0.7563
Epoch 75/100
1/640 [..............................] - ETA: 3s - loss: 0.8623 - accuracy: 0.0000e+00
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4680 - accuracy: 0.8141 - val_loss: 0.4965 - val_accuracy: 0.7563
Epoch 76/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4676 - accuracy: 0.8266 - val_loss: 0.5035 - val_accuracy: 0.7688
Epoch 77/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4652 - accuracy: 0.8266 - val_loss: 0.5068 - val_accuracy: 0.7563
Epoch 78/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4693 - accuracy: 0.8203 - val_loss: 0.4972 - val_accuracy: 0.7625
Epoch 79/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4673 - accuracy: 0.8109 - val_loss: 0.4955 - val_accuracy: 0.7625
Epoch 80/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4685 - accuracy: 0.8203 - val_loss: 0.4931 - val_accuracy: 0.7875
Epoch 81/100
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640/640 [==============================] - 2s 4ms/sample - loss: 0.4673 - accuracy: 0.8172 - val_loss: 0.5024 - val_accuracy: 0.7812
Epoch 82/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4669 - accuracy: 0.8219 - val_loss: 0.4955 - val_accuracy: 0.7812
Epoch 83/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4637 - accuracy: 0.8109 - val_loss: 0.4999 - val_accuracy: 0.7500
Epoch 84/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4660 - accuracy: 0.8219 - val_loss: 0.4932 - val_accuracy: 0.7688
Epoch 85/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4652 - accuracy: 0.8203 - val_loss: 0.4988 - val_accuracy: 0.7937
Epoch 86/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4644 - accuracy: 0.8266 - val_loss: 0.4969 - val_accuracy: 0.7625
Epoch 87/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4636 - accuracy: 0.8172 - val_loss: 0.4921 - val_accuracy: 0.7688
Epoch 88/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4643 - accuracy: 0.8203 - val_loss: 0.5042 - val_accuracy: 0.7937
Epoch 89/100
1/640 [..............................] - ETA: 5s - loss: 4.8876e-06 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4602 - accuracy: 0.8234 - val_loss: 0.5158 - val_accuracy: 0.7875
Epoch 90/100
1/640 [..............................] - ETA: 5s - loss: 0.1601 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4655 - accuracy: 0.8219 - val_loss: 0.4975 - val_accuracy: 0.7875
Epoch 91/100
1/640 [..............................] - ETA: 1s - loss: 0.2297 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4648 - accuracy: 0.8203 - val_loss: 0.4933 - val_accuracy: 0.7875
Epoch 92/100
1/640 [..............................] - ETA: 5s - loss: 0.0200 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4575 - accuracy: 0.8328 - val_loss: 0.5326 - val_accuracy: 0.7750
Epoch 93/100
1/640 [..............................] - ETA: 7s - loss: 0.4895 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4637 - accuracy: 0.8281 - val_loss: 0.4968 - val_accuracy: 0.7688
Epoch 94/100
1/640 [..............................] - ETA: 4s - loss: 0.3766 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4628 - accuracy: 0.8203 - val_loss: 0.4964 - val_accuracy: 0.7625
Epoch 95/100
1/640 [..............................] - ETA: 2s - loss: 0.5306 - accuracy: 1.0000
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4631 - accuracy: 0.8203 - val_loss: 0.5032 - val_accuracy: 0.7625
Epoch 96/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4642 - accuracy: 0.8250 - val_loss: 0.5130 - val_accuracy: 0.7875
Epoch 97/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4639 - accuracy: 0.8203 - val_loss: 0.5047 - val_accuracy: 0.7812
Epoch 98/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4628 - accuracy: 0.8203 - val_loss: 0.4946 - val_accuracy: 0.7750
Epoch 99/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4593 - accuracy: 0.8281 - val_loss: 0.4884 - val_accuracy: 0.7750
Epoch 100/100
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640/640 [==============================] - 2s 3ms/sample - loss: 0.4618 - accuracy: 0.8219 - val_loss: 0.4949 - val_accuracy: 0.7750model %>% evaluate(as.matrix(X_test), y_test.ohe)
200/1 [========================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================
$accuracy
[1] 0.84pred.class <- model %>% predict_classes(as.matrix(X_test))
pred.prob <- model %>% predict(as.matrix(X_test)) %>% round(3)
conf <- confusionMatrix(as.factor(y_test), as.factor(pred.class))
#(i)Overall Accuracy
cat(paste0("Overall Accuracy"))Overall Accuracyprint(conf$overall[1])Accuracy
0.84 #(ii)Per-class sensitivity, specificity, and balanced accuracy on the test dataset
cat(paste0("Per-class sensitivity"))Per-class sensitivityprint(conf$byClass[,1])Class: 0 Class: 1 Class: 2 Class: 3
0.9000 0.8148 0.7755 0.8649 cat(paste0("Per-class Specificity"))Per-class Specificityprint(conf$byClass[,2])Class: 0 Class: 1 Class: 2 Class: 3
0.9786 0.9384 0.9735 0.9018 cat(paste0("Per-class Balance accuracy"))Per-class Balance accuracyprint(conf$byClass[,11])Class: 0 Class: 1 Class: 2 Class: 3
0.9393 0.8766 0.8745 0.8834 ## Adding new hidden layer improved the performance when compared with 2.2.(a)
# first hidden layer captures simple feature. Adding more hidden layer helps to capture specific patterns of data.Part 2.3
MY_IIT_ID <- 20458051
user <- MY_IIT_ID %% 671
paste("user selected: ", user)[1] "user selected: 603"###randomly choose 10 movies from the movies.csv file
set.seed(100)
movies <- read.csv("movies.csv", header=T, sep=",")
index <- sample(1:nrow(movies), 10)
movies_selected <- movies[index, ]
cat(paste0("\n"))sprintf("10 randomly selected movies (movieID): ")[1] "10 randomly selected movies (movieID): "movies_selected$movieId [1] 4849 561 4324 4714 5356 89408 3816
[8] 98154 113829 2486Build User Profile
ratings_table <- read.csv("/Users/saumi/Desktop/422/homeworks/Homework 4/ml-latest-small/ratings.csv",header=T, sep=",")
user603 <- subset(ratings_table, ratings_table[,1] == user)
movie_id <- user603[,2]
n <- length(movie_id)
x <- 0
x[1:n] <- 0
user_profile <- data.frame(movie_id,Action=x, Adventure=x, Animation=x, Children=x, Comedy=x, Crime=x, Documentary=x, Drama=x, Fantasy=x,`Film-Noir`=x, Horror=x, IMAX=x, Musical=x, Mystery=x, Romance=x, `Sci-Fi`=x, Thriller=x, War=x, Western=x,`no genres listed`=x)
names <- names(user_profile)
library(stringr)
names_new <- str_replace_all(names,c("Film.Noir"="Film-Noir","Sci.Fi"="Sci-Fi","no.genres.listed"="(no genres listed)"))
names(user_profile) <- names_new
genre_list <- c("Action", "Adventure", "Animation", "Children", "Comedy", "Crime", "Documentary", "Drama", "Fantasy","Film-Noir", "Horror", "IMAX", "Musical", "Mystery", "Romance", "Sci-Fi", "Thriller", "War", "Western","(no genres listed)")
for (row in 1:nrow(user_profile)) {
genre <- as.list(strsplit(toString(movies[movies$movieId == user_profile[row, ]$movie_id, ]$genres),"[|]")[[1]])
for (g in genre) {
user_profile[row, g] = 1
}
}
for (g in genre_list) {
user_profile[n+1,] <- round(colMeans(user_profile),4)
}
user_profile[n+1,1] <- "AVG"
user_profile
cat(paste0("User Profile Vectors are as follows: "))User Profile Vectors are as follows: cat(paste0(user_profile[nrow(user_profile),2:21]))0.386 0.3772 0.1579 0.307 0.4123 0.0789 0 0.2193 0.2456 0 0.0263 0.0263 0.1754 0.0175 0.1491 0.193 0.193 0.0439 0.0263 0Build Movie profile
movie_id1 <- movies_selected$movieId
n1 <- length(movie_id1)
x <- 0
x[1:n1] <- 0
movie_profile <- data.frame(movie_id1,Action=x, Adventure=x, Animation=x, Children=x, Comedy=x, Crime=x, Documentary=x, Drama=x, Fantasy=x,`Film-Noir`=x, Horror=x, IMAX=x, Musical=x, Mystery=x, Romance=x, `Sci-Fi`=x, Thriller=x, War=x, Western=x,`no genres listed`=x)
names1 <- names(movie_profile)
library(stringr)
names_new1 <- str_replace_all(names1,c("Film.Noir"="Film-Noir","Sci.Fi"="Sci-Fi","no.genres.listed"="(no genres listed)"))
names(movie_profile) <- names_new1
genre_list1 <- c("Action", "Adventure", "Animation", "Children", "Comedy", "Crime", "Documentary", "Drama", "Fantasy","Film-Noir", "Horror", "IMAX", "Musical", "Mystery", "Romance", "Sci-Fi", "Thriller", "War", "Western","(no genres listed)")
for (row in 1:nrow(movie_profile)) {
genre1 <- as.list(strsplit(toString(movies[movies$movieId == movie_profile[row, ]$movie_id1, ]$genres),"[|]")[[1]])
for (g1 in genre1) {
movie_profile[row, g1] = 1
}
}
movie_profileCosine Similarity
cos_sim <- NULL
options("digits"=4)
for(i in 1:nrow(movie_profile)){
Similarity <- lsa::cosine(as.numeric(user_profile[n+1,-1]), as.numeric(movie_profile[movie_profile$movie_id1 == movie_profile[i,"movie_id1"],-1]))[[1]]
movieId <- movie_profile[movie_profile$movie_id1 == movie_profile[i,"movie_id1"],1]
MovieName <- as.vector(movies[movies$movieId == movieId,"title"])[1]
#cat("Movie:", title, ", and similarity score:", similarity , "\n")
tmp <- data.frame(movieId,MovieName,Similarity)
cos_sim <- rbind(cos_sim,tmp)
}
print(cos_sim, row.names = FALSE)Recommended Movies for User
recommended <- head(cos_sim[order(-cos_sim[,3]),],5)
cat(paste0("User ID: ", user," chose the following 10 movies:"))User ID: 603 chose the following 10 movies:cat(paste0("\n"))print(movie_id1) [1] 4849 561 4324 4714 5356 89408 3816
[8] 98154 113829 2486cat(paste0("\n"))cat(paste0("Of these, the following 5 movies are recommended:"))Of these, the following 5 movies are recommended:print.data.frame(recommended,row.names = FALSE) movieId MovieName Similarity
89408 April Love (1957) 0.5124
113829 One I Love, The (2014) 0.4957
4849 My First Mister (2001) 0.4911
2486 24 Hour Woman, The (1998) 0.4911
4714 Any Which Way You Can (1980) 0.4534LS0tDQp0aXRsZTogIkNTIDQyMiINCm91dHB1dDogDQogIGh0bWxfbm90ZWJvb2sgOg0KICAgIHRvYzogeWVzDQogICAgdG9jX2Zsb2F0OiB5ZXMNCmF1dGhvcjogU25laGEgU2hhaGFqaSBLYWRhbQ0KZWRpdG9yX29wdGlvbnM6IA0KICBjaHVua19vdXRwdXRfdHlwZTogaW5saW5lDQotLS0NCg0KIyMjIFBhcnQgMi4xDQpgYGB7cn0NCnJtKGxpc3Q9bHMoKSkNCmNvdW50cnlfZGYgPC0gcmVhZC5jc3YoImNvdW50cmllcy5jc3YiLGhlYWRlcj1UUlVFLHJvdy5uYW1lcz0xKQ0KYGBgDQojIyMgUGFydCAyLjEoYSkoaSkNCmBgYHtyfQ0Kc3VtbWFyeShjb3VudHJ5X2RmKQ0KYGBgDQojIyMgUGFydCAyLjEoYSkoaWkpDQpgYGB7cn0NCmJveHBsb3QoY291bnRyeV9kZiRHRFAsbWFpbj0iQm94cGxvdCBmb3IgR0RQIikNCmJveHBsb3QoY291bnRyeV9kZiRISVYsbWFpbj0iQm94cGxvdCBmb3IgSElWIikNCmJveHBsb3QoY291bnRyeV9kZiRMaWZlZXhwLG1haW49IkJveHBsb3QgZm9yIExpZmVleHAiKQ0KYm94cGxvdChjb3VudHJ5X2RmJE1pbCxtYWluPSJCb3hwbG90IGZvciBNaWwiKQ0KYm94cGxvdChjb3VudHJ5X2RmJE9pbGNvbnMsbWFpbj0iQm94cGxvdCBmb3IgT2lsY29ucyIpDQpib3hwbG90KGNvdW50cnlfZGYkUG9wLG1haW49IkJveHBsb3QgZm9yIFBvcCIpDQpib3hwbG90KGNvdW50cnlfZGYkVGVsLG1haW49IkJveHBsb3QgZm9yIFRlbCIpDQpib3hwbG90KGNvdW50cnlfZGYkVW5lbXBsLG1haW49IkJveHBsb3QgZm9yIFVuZW1wbCIpDQoNCiMjIEJveHBsb3QgZm9yIFBvcCBoYXMgMiBvdXRsaWVycy5UaG9zZSAyIG9ic2VydmF0aW9ucyBhcmUgbnVtZXJpY2FsbHkgZGlzdGFudCBmcm9tIHRoZSByZXN0IG9mIHRoZSBkYXRhLlRoaXMgYXR0cmlidXRlIGNhcHR1cmVzIG1vc3Qgb2YgdGhlIHZhcmlhbmNlLg0KYGBgDQoNCiMjIyBQYXJ0IDIuMShiKQ0KYGBge3J9DQpwY2FfY291bnRyeV9kZiA8LSBwcmNvbXAoY291bnRyeV9kZixzY2FsZS49VCkNCmBgYA0KIyMjIFBhcnQgMi4xKGMpKGkpDQpgYGB7cn0NCnN1bW1hcnkocGNhX2NvdW50cnlfZGYpDQojRmlyc3QgZm91ciBjb21wb25lbnRzIGV4cGxhaW5zIGF0bGVhc3QgOTAlIG9mIHRoZSB2YXJpYW5jZS4NCmBgYA0KIyMjIFBhcnQgMi4xKGMpKGlpKQ0KYGBge3J9DQpzY3JlZXBsb3QocGNhX2NvdW50cnlfZGYsdHlwZT0ibGluZXMiKQ0KYGBgDQojIyMgUGFydCAyLjEoYykoaWlpKQ0KYGBge3J9DQojQmFzZWQgb24gdGhlIHNjcmVlcGxvdCwgd2UnbGwgY2hvb3NlIGZpcnN0IGZvdXIgY29tcG9uZW50cyB0byB1c2UgZm9yIG1vZGVsaW5nIHRvIGVuZ2FnZSBpbiBhIGZlYXR1cmUgcmVkdWN0aW9uIHRhc2suDQpgYGANCiMjIyBQYXJ0IDIuMShkKQ0KYGBge3J9DQpyb3RhdGlvbiA8LSBwY2FfY291bnRyeV9kZiRyb3RhdGlvbg0Kcm90YXRpb24NCiNyb3RhdGlvbiA8LSAtcm90YXRpb24NCiNwY2FfY291bnRyeV9kZiR4IDwtIC1wY2FfY291bnRyeV9kZiR4DQpgYGANCiMjIyBQYXJ0IDIuMShkKShpKQ0KYGBge3J9DQojUEMxIGlzIHBvc2l0aXZlbHkgY29ycmVsYXRlZCB0byBHRFAsIExpZmVleHAsIE9pbGNvbnMsIFRlbCBhdHRyaWJ1dGVzLg0KI1BDMSBpcyBuZWdhdGl2ZWx5IGNvcnJlbGF0ZWQgdG8gSElWLCBNaWwsIFBvcCwgVW5lbXBsIGF0dHJpYnV0ZXMuDQpgYGANCiMjIyBQYXJ0IDIuMShkKShpaSkNCmBgYHtyfQ0KI1BDMiBpcyBwb3NpdGl2ZWx5IGNvcnJlbGF0ZWQgdG8gR0RQLCBMaWZlZXhwLE1pbCwgT2lsY29ucyxQb3AsIFRlbCBhdHRyaWJ1dGVzLg0KI1BDMiBpcyBuZWdhdGl2ZWx5IGNvcnJlbGF0ZWQgdG8gSElWLCBVbmVtcGwgYXR0cmlidXRlcy4NCmBgYA0KDQojIyMgUGFydCAyLjEoZSkNCmBgYHtyfQ0KYmlwbG90KHBjYV9jb3VudHJ5X2RmLHNjYWxlPTApDQpgYGANCiMjIyBQYXJ0IDIuMShlKShpKQ0KYGBge3J9DQpwY2FfY291bnRyeV9kZiR4W2MoMSwxNCw5KSxjKDEsMildDQpgYGANCiMjIyBQYXJ0IDIuMShlKShpaSkNCmBgYHtyfQ0KI1BDMSBpcyBoaWdoIGZvciBKYXBhbiBhbmQgbG93ZXN0IGZvciBCcmF6aWwuDQojUEMxIGlzIHBvc2l0aXZlbHkgY29ycmVsYXRlZCB0byBHRFAsIExpZmVleHAsIE9pbGNvbnMsIFRlbCBhdHRyaWJ1dGVzLg0KI0phcGFuIGhhcyBoaWdoZXIgdmFsdWVzIGluIEdEUCxMaWZlRXhwLCBPaWxjb25zLFRlbCBhdHRyaWJ1dGVzIGNvbXBhcmUgdG8gQnJhemlsIGFuZCBVSy4NCg0KI1BDMiBpcyBoaWdoIGZvciBVSyBhbmQgbG93ZXN0IGZvciBCcmF6aWwuDQojUEMyIGlzIHBvc2l0aXZlbHkgY29ycmVsYXRlZCB0byBHRFAsIExpZmVleHAsTWlsLCBPaWxjb25zLFBvcCwgVGVsIGF0dHJpYnV0ZXMuDQojVUsgaGFzIGhpZ2hlciB2YWx1ZXMgaW4gR0RQLCBMaWZlZXhwLE1pbCwgT2lsY29ucyxQb3AsIFRlbCBhdHRyaWJ1dGVzIGNvbXBhcmUgdG8gSmFwYW4gYW5kIEJyYXppbC4NCg0KI1BDMSBhbmQgUEMyIGZvciBCcmF6aWwsIFVLLCBhbmQgSmFwYW4gbWFrZSBzZW5zZS4NCmBgYA0KDQojIyMgUGFydCAyLjINCmBgYHtyfQ0KbGlicmFyeSh0ZW5zb3JmbG93KQ0KbGlicmFyeShrZXJhcykNCmxpYnJhcnkoZHBseXIpDQpsaWJyYXJ5KGNhcmV0KQ0KDQpybShsaXN0PWxzKCkpDQpkZiA8LSByZWFkLmNzdigiYWN0aXZpdHktc21hbGwuY3N2IikNCg0Kc2V0LnNlZWQoMTEyMikNCmRmIDwtIGRmW3NhbXBsZShucm93KGRmKSksIF0gDQoNCmluZHggPC0gc2FtcGxlKDE6bnJvdyhkZiksIDAuMjAqbnJvdyhkZikpDQp0ZXN0LmRmICA8LSBkZltpbmR4LCBdDQp0cmFpbi5kZiA8LSBkZlstaW5keCwgXQ0KDQpsYWJlbC50ZXN0IDwtIHRlc3QuZGYkbGFiZWwNCnRlc3QuZGYkbGFiZWwgPC0gTlVMTA0KdGVzdC5kZiA8LSBhcy5kYXRhLmZyYW1lKHNjYWxlKHRlc3QuZGYpKQ0KdGVzdC5kZiRsYWJlbCA8LSBsYWJlbC50ZXN0DQpybShsYWJlbC50ZXN0KQ0KDQpsYWJlbC50cmFpbiA8LSB0cmFpbi5kZiRsYWJlbA0KdHJhaW4uZGYkbGFiZWwgPC0gTlVMTA0KdHJhaW4uZGYgPC0gYXMuZGF0YS5mcmFtZShzY2FsZSh0cmFpbi5kZikpDQp0cmFpbi5kZiRsYWJlbCA8LSBsYWJlbC50cmFpbg0Kcm0obGFiZWwudHJhaW4pDQpybShpbmR4KQ0KYGBgDQoNCiMjIyBQYXJ0IDIuMiAoYSkNCmBgYHtyfQ0KWF90cmFpbiA8LSBzZWxlY3QodHJhaW4uZGYsIC1sYWJlbCkNCnlfdHJhaW4gPC0gdHJhaW4uZGYkbGFiZWwNCg0KeV90cmFpbi5vaGUgPC0gdG9fY2F0ZWdvcmljYWwoeV90cmFpbikNClhfdGVzdCA8LSBzZWxlY3QodGVzdC5kZiwgLWxhYmVsKQ0KeV90ZXN0IDwtIHRlc3QuZGYkbGFiZWwNCnlfdGVzdC5vaGUgPC0gdG9fY2F0ZWdvcmljYWwodGVzdC5kZiRsYWJlbCkNCg0KbW9kZWwgPC0ga2VyYXNfbW9kZWxfc2VxdWVudGlhbCgpICU+JQ0KICBsYXllcl9kZW5zZSh1bml0cyA9IDEwLCBhY3RpdmF0aW9uPSJyZWx1IiwgaW5wdXRfc2hhcGU9YygzKSkgJT4lDQogIGxheWVyX2RlbnNlKHVuaXRzID0gNCwgYWN0aXZhdGlvbj0ic29mdG1heCIpDQoNCm1vZGVsICU+JSANCiAgY29tcGlsZShsb3NzID0gImNhdGVnb3JpY2FsX2Nyb3NzZW50cm9weSIsIG9wdGltaXplcj0iYWRhbSIsIG1ldHJpY3M9YygiYWNjdXJhY3kiKSkNCg0KbW9kZWwgJT4lIGZpdCgNCiAgZGF0YS5tYXRyaXgoWF90cmFpbiksIA0KICB5X3RyYWluLm9oZSwNCiAgZXBvY2hzPTEwMCwNCiAgYmF0Y2hfc2l6ZT0xLA0KICB2YWxpZGF0aW9uX3NwbGl0PTAuMjANCikNCg0KbW9kZWwgJT4lIGV2YWx1YXRlKGFzLm1hdHJpeChYX3Rlc3QpLCB5X3Rlc3Qub2hlKQ0KDQpwcmVkLmNsYXNzICA8LSBtb2RlbCAlPiUgcHJlZGljdF9jbGFzc2VzKGFzLm1hdHJpeChYX3Rlc3QpKQ0KcHJlZC5wcm9iICAgPC0gbW9kZWwgJT4lIHByZWRpY3QoYXMubWF0cml4KFhfdGVzdCkpICU+JSByb3VuZCgzKQ0KDQpjb25mIDwtIGNvbmZ1c2lvbk1hdHJpeChhcy5mYWN0b3IoeV90ZXN0KSwgYXMuZmFjdG9yKHByZWQuY2xhc3MpKQ0KIyhpKU92ZXJhbGwgQWNjdXJhY3kNCmNhdChwYXN0ZTAoIk92ZXJhbGwgQWNjdXJhY3kiKSkNCnByaW50KGNvbmYkb3ZlcmFsbFsxXSkNCiMoaWkpUGVyLWNsYXNzIHNlbnNpdGl2aXR5LCBzcGVjaWZpY2l0eSwgYW5kIGJhbGFuY2VkIGFjY3VyYWN5IG9uIHRoZSB0ZXN0IGRhdGFzZXQNCmNhdChwYXN0ZTAoIlBlci1jbGFzcyBzZW5zaXRpdml0eSIpKQ0KcHJpbnQoY29uZiRieUNsYXNzWywxXSkNCmNhdChwYXN0ZTAoIlBlci1jbGFzcyBTcGVjaWZpY2l0eSIpKQ0KcHJpbnQoY29uZiRieUNsYXNzWywyXSkNCmNhdChwYXN0ZTAoIlBlci1jbGFzcyBCYWxhbmNlIGFjY3VyYWN5IikpDQpwcmludChjb25mJGJ5Q2xhc3NbLDExXSkNCmBgYA0KDQojIyMgcGFydCAyLjIoYikNCmBgYHtyfQ0Kcm0obW9kZWwpDQpiYXRjaCA8LSBjKDEsMzIsNjQsMTI4LDI1NikNCmkgPC0gMQ0KDQpmb3IgKGkgaW4gMTo1KQ0Kew0KICBtb2RlbCA8LSBrZXJhc19tb2RlbF9zZXF1ZW50aWFsKCkgJT4lDQogIGxheWVyX2RlbnNlKHVuaXRzID0gMTAsIGFjdGl2YXRpb249InJlbHUiLCBpbnB1dF9zaGFwZT1jKDMpKSAlPiUNCiAgbGF5ZXJfZGVuc2UodW5pdHMgPSA0LCBhY3RpdmF0aW9uPSJzb2Z0bWF4IikNCg0KbW9kZWwgJT4lIA0KICBjb21waWxlKGxvc3MgPSAiY2F0ZWdvcmljYWxfY3Jvc3NlbnRyb3B5Iiwgb3B0aW1pemVyPSJhZGFtIiwgbWV0cmljcz1jKCJhY2N1cmFjeSIpKQ0KDQogIGNhdChwYXN0ZTAoIm5ldXJhbCBuZXR3b3JrIGZvciBiYXRjaCBzaXplOiAiLGJhdGNoW2ldKSkNCiAgY2F0KHBhc3RlMCgiXG4iKSkNCiAgYmVnaW4gPC0gU3lzLnRpbWUoKQ0KbW9kZWwgJT4lIGZpdCgNCiAgZGF0YS5tYXRyaXgoWF90cmFpbiksIA0KICB5X3RyYWluLm9oZSwNCiAgZXBvY2hzPTEwMCwNCiAgYmF0Y2hfc2l6ZT1iYXRjaFtpXSwNCiAgdmFsaWRhdGlvbl9zcGxpdD0wLjIwDQopDQplbmQgPC0gU3lzLnRpbWUoKQ0KdGltZV9lbGFwc2VkIDwtIGVuZCAtIGJlZ2luDQpjYXQocGFzdGUwKCJUaW1lIHRha2VuIHRvIHRyYWluIG1vZGVsOiAiLHRpbWVfZWxhcHNlZCkpDQppPWkrMQ0KbW9kZWwgJT4lIGV2YWx1YXRlKGFzLm1hdHJpeChYX3Rlc3QpLCB5X3Rlc3Qub2hlKQ0KDQpwcmVkLmNsYXNzICA8LSBtb2RlbCAlPiUgcHJlZGljdF9jbGFzc2VzKGFzLm1hdHJpeChYX3Rlc3QpKQ0KcHJlZC5wcm9iICAgPC0gbW9kZWwgJT4lIHByZWRpY3QoYXMubWF0cml4KFhfdGVzdCkpICU+JSByb3VuZCgzKQ0KDQpjb25mIDwtIGNvbmZ1c2lvbk1hdHJpeChhcy5mYWN0b3IoeV90ZXN0KSwgYXMuZmFjdG9yKHByZWQuY2xhc3MpKQ0KcHJpbnQoY29uZikNCnJtKG1vZGVsKQ0KfQ0KYGBgDQojIyMgVGFibGUgZm9yIFRpbWUgaXQgdG9vayB0byB0cmFpbiB0aGUgbmV0d29yayB3aXRoIGRpZmZlcmVudCBiYXRjaCBzaXplDQpgYGB7cn0NCkJhdGNoX1NpemUgPC0gYygxLDMyLDY0LDEyOCwyNjQpDQpUaW1lX1NlYyA8LSBjKDE5Miw1NC4wMiw1Mi43OSw1MS44NCw1MS4yMSkNCnRpbWVfdGFibGUgPC0gZGF0YS5mcmFtZShCYXRjaF9TaXplLFRpbWVfU2VjKQ0KY29sbmFtZXModGltZV90YWJsZSkgPC0gYygiQmF0Y2ggU2l6ZSIsIlRpbWUgKGluIHNlY29uZHMpIikNCmNhdChwYXN0ZTAoIlRpbWUgaXQgdG9vayB0byB0cmFpbiB0aGUgbmV0d29yayB3aXRoIGRpZmZlcmVudCBiYXRjaCBzaXplIGlzIGFzIGZvbGxvd3M6IikpDQpwcmludCh0aW1lX3RhYmxlKQ0KDQojV2l0aCBpbmNyZWFzZSBpbiBiYXRjaCBzaXplLCB0aW1lIHRha2VuIHRvIHRyYWluIG5ldHdvcmsgcmVkdWNlcy4NCmBgYA0KDQojIyMgUGFydCAyLjIgKGMpDQpgYGB7cn0NCmFjYyA8LSBjKDAuODEsMC43OCwwLjc3NSwwLjY5NSwwLjY0KQ0KDQpjbGFzczAgPC0gYygwLjk0MzYsMC45NDM2LDAuOTMzOCwwLjkxNDIsMC45MDIwKQ0KY2xhc3MxIDwtIGMoMC44MzM5LDAuODE2NCwwLjc5LDAuNzU0NSwwLjY0NDUpDQpjbGFzczIgPC0gYygwLjg2OTAsMC44MzU2LDAuOTAwMSwwLjc2OTEsMC44NDk1KQ0KY2xhc3MzIDwtIGMoMC44NjM4LDAuODIzNSwwLjc1NjMsMC43MzE0LDAuNjMzOCkNCg0KbmV3X3RhYmxlIDwtIGRhdGEuZnJhbWUoQmF0Y2hfU2l6ZSxhY2MsY2xhc3MwLGNsYXNzMSxjbGFzczIsY2xhc3MzKQ0KDQpjb2xuYW1lcyhuZXdfdGFibGUpIDwtIGMoIkJhdGNoIFNpemUiLCJPdmVyYWxsIEFjY3VyYWN5IiwiQmFsYW5jZWQgQWNjdXJhY3kgZm9yIGNsYXNzMCIsIkJhbGFuY2VkIEFjY3VyYWN5IGZvciBjbGFzczEiLCJCYWxhbmNlZCBBY2N1cmFjeSBmb3IgY2xhc3MyIiwiQmFsYW5jZWQgQWNjdXJhY3kgZm9yIGNsYXNzMyIpDQoNCnByaW50KG5ld190YWJsZSkNCg0KI092ZXJhbGwgYWNjdXJhY3kgZGVjcmVhc2VzIHdpdGggaW5jcmVhc2UgaW4gYmF0Y2ggc2l6ZS4NCiNCYWxhbmNlZCBhY2N1cmFjeSBpbiBjbGFzcyAwLDEsMyBkZWNyZWFzZXMgd2l0aCBpbmNyZWFzZSBpbiBiYXRjaCBzaXplLg0KYGBgDQoNCiMjIyBQYXJ0IDIuMiAoZCkNCmBgYHtyfQ0KbW9kZWwgPC0ga2VyYXNfbW9kZWxfc2VxdWVudGlhbCgpICU+JSBsYXllcl9kZW5zZSh1bml0cyA9IDEwLCBhY3RpdmF0aW9uPSJyZWx1IiwgaW5wdXRfc2hhcGU9YygzKSkgJT4lIGxheWVyX2RlbnNlKHVuaXRzID01LCBhY3RpdmF0aW9uPSJyZWx1IikgJT4lIGxheWVyX2RlbnNlKHVuaXRzID0gNCwgYWN0aXZhdGlvbj0ic29mdG1heCIpDQoNCm1vZGVsICU+JSANCiAgY29tcGlsZShsb3NzID0gImNhdGVnb3JpY2FsX2Nyb3NzZW50cm9weSIsIG9wdGltaXplcj0iYWRhbSIsIG1ldHJpY3M9YygiYWNjdXJhY3kiKSkNCg0KbW9kZWwgJT4lIGZpdCgNCiAgZGF0YS5tYXRyaXgoWF90cmFpbiksIA0KICB5X3RyYWluLm9oZSwNCiAgZXBvY2hzPTEwMCwNCiAgYmF0Y2hfc2l6ZT0xLA0KICB2YWxpZGF0aW9uX3NwbGl0PTAuMjANCikNCg0KbW9kZWwgJT4lIGV2YWx1YXRlKGFzLm1hdHJpeChYX3Rlc3QpLCB5X3Rlc3Qub2hlKQ0KDQpwcmVkLmNsYXNzICA8LSBtb2RlbCAlPiUgcHJlZGljdF9jbGFzc2VzKGFzLm1hdHJpeChYX3Rlc3QpKQ0KcHJlZC5wcm9iICAgPC0gbW9kZWwgJT4lIHByZWRpY3QoYXMubWF0cml4KFhfdGVzdCkpICU+JSByb3VuZCgzKQ0KDQpjb25mIDwtIGNvbmZ1c2lvbk1hdHJpeChhcy5mYWN0b3IoeV90ZXN0KSwgYXMuZmFjdG9yKHByZWQuY2xhc3MpKQ0KIyhpKU92ZXJhbGwgQWNjdXJhY3kNCmNhdChwYXN0ZTAoIk92ZXJhbGwgQWNjdXJhY3kiKSkNCnByaW50KGNvbmYkb3ZlcmFsbFsxXSkNCiMoaWkpUGVyLWNsYXNzIHNlbnNpdGl2aXR5LCBzcGVjaWZpY2l0eSwgYW5kIGJhbGFuY2VkIGFjY3VyYWN5IG9uIHRoZSB0ZXN0IGRhdGFzZXQNCmNhdChwYXN0ZTAoIlBlci1jbGFzcyBzZW5zaXRpdml0eSIpKQ0KcHJpbnQoY29uZiRieUNsYXNzWywxXSkNCmNhdChwYXN0ZTAoIlBlci1jbGFzcyBTcGVjaWZpY2l0eSIpKQ0KcHJpbnQoY29uZiRieUNsYXNzWywyXSkNCmNhdChwYXN0ZTAoIlBlci1jbGFzcyBCYWxhbmNlIGFjY3VyYWN5IikpDQpwcmludChjb25mJGJ5Q2xhc3NbLDExXSkNCg0KIyMgQWRkaW5nIG5ldyBoaWRkZW4gbGF5ZXIgaW1wcm92ZWQgdGhlIHBlcmZvcm1hbmNlIHdoZW4gY29tcGFyZWQgd2l0aCAyLjIuKGEpDQojIGZpcnN0IGhpZGRlbiBsYXllciBjYXB0dXJlcyBzaW1wbGUgZmVhdHVyZS4gQWRkaW5nIG1vcmUgaGlkZGVuIGxheWVyIGhlbHBzIHRvIGNhcHR1cmUgc3BlY2lmaWMgcGF0dGVybnMgb2YgZGF0YS4NCmBgYA0KDQojIyMgUGFydCAyLjMNCmBgYHtyfQ0KTVlfSUlUX0lEIDwtIDIwNDU4MDUxDQp1c2VyIDwtIE1ZX0lJVF9JRCAlJSA2NzENCnBhc3RlKCJ1c2VyIHNlbGVjdGVkOiAiLCB1c2VyKQ0KYGBgDQojIyNyYW5kb21seSBjaG9vc2UgMTAgbW92aWVzIGZyb20gdGhlIG1vdmllcy5jc3YgZmlsZQ0KYGBge3J9DQpzZXQuc2VlZCgxMDApDQptb3ZpZXMgPC0gcmVhZC5jc3YoIm1vdmllcy5jc3YiLCBoZWFkZXI9VCwgc2VwPSIsIikNCmluZGV4IDwtIHNhbXBsZSgxOm5yb3cobW92aWVzKSwgMTApDQptb3ZpZXNfc2VsZWN0ZWQgPC0gbW92aWVzW2luZGV4LCBdDQpjYXQocGFzdGUwKCJcbiIpKQ0Kc3ByaW50ZigiMTAgcmFuZG9tbHkgc2VsZWN0ZWQgbW92aWVzIChtb3ZpZUlEKTogIikNCm1vdmllc19zZWxlY3RlZCRtb3ZpZUlkDQpgYGANCiMjIyBCdWlsZCBVc2VyIFByb2ZpbGUNCmBgYHtyfQ0KcmF0aW5nc190YWJsZSA8LSByZWFkLmNzdigiL1VzZXJzL3NhdW1pL0Rlc2t0b3AvNDIyL2hvbWV3b3Jrcy9Ib21ld29yayA0L21sLWxhdGVzdC1zbWFsbC9yYXRpbmdzLmNzdiIsaGVhZGVyPVQsIHNlcD0iLCIpDQoNCnVzZXI2MDMgPC0gc3Vic2V0KHJhdGluZ3NfdGFibGUsIHJhdGluZ3NfdGFibGVbLDFdID09IHVzZXIpDQptb3ZpZV9pZCA8LSB1c2VyNjAzWywyXQ0KDQpuIDwtIGxlbmd0aChtb3ZpZV9pZCkNCnggPC0gMA0KeFsxOm5dIDwtIDANCnVzZXJfcHJvZmlsZSA8LSBkYXRhLmZyYW1lKG1vdmllX2lkLEFjdGlvbj14LCBBZHZlbnR1cmU9eCwgQW5pbWF0aW9uPXgsIENoaWxkcmVuPXgsIENvbWVkeT14LCBDcmltZT14LCBEb2N1bWVudGFyeT14LCBEcmFtYT14LCBGYW50YXN5PXgsYEZpbG0tTm9pcmA9eCwgSG9ycm9yPXgsIElNQVg9eCwgTXVzaWNhbD14LCBNeXN0ZXJ5PXgsIFJvbWFuY2U9eCwgYFNjaS1GaWA9eCwgVGhyaWxsZXI9eCwgV2FyPXgsIFdlc3Rlcm49eCxgbm8gZ2VucmVzIGxpc3RlZGA9eCkNCg0KbmFtZXMgPC0gbmFtZXModXNlcl9wcm9maWxlKQ0KbGlicmFyeShzdHJpbmdyKQ0KbmFtZXNfbmV3IDwtIHN0cl9yZXBsYWNlX2FsbChuYW1lcyxjKCJGaWxtLk5vaXIiPSJGaWxtLU5vaXIiLCJTY2kuRmkiPSJTY2ktRmkiLCJuby5nZW5yZXMubGlzdGVkIj0iKG5vIGdlbnJlcyBsaXN0ZWQpIikpDQpuYW1lcyh1c2VyX3Byb2ZpbGUpIDwtIG5hbWVzX25ldw0KDQpnZW5yZV9saXN0IDwtIGMoIkFjdGlvbiIsICJBZHZlbnR1cmUiLCAiQW5pbWF0aW9uIiwgIkNoaWxkcmVuIiwgIkNvbWVkeSIsICJDcmltZSIsICJEb2N1bWVudGFyeSIsICJEcmFtYSIsICJGYW50YXN5IiwiRmlsbS1Ob2lyIiwgIkhvcnJvciIsICJJTUFYIiwgIk11c2ljYWwiLCAiTXlzdGVyeSIsICJSb21hbmNlIiwgIlNjaS1GaSIsICJUaHJpbGxlciIsICJXYXIiLCAiV2VzdGVybiIsIihubyBnZW5yZXMgbGlzdGVkKSIpDQoNCmZvciAocm93IGluIDE6bnJvdyh1c2VyX3Byb2ZpbGUpKSB7DQogIGdlbnJlIDwtIGFzLmxpc3Qoc3Ryc3BsaXQodG9TdHJpbmcobW92aWVzW21vdmllcyRtb3ZpZUlkID09IHVzZXJfcHJvZmlsZVtyb3csIF0kbW92aWVfaWQsIF0kZ2VucmVzKSwiW3xdIilbWzFdXSkNCiAgZm9yIChnIGluIGdlbnJlKSB7DQogICAgdXNlcl9wcm9maWxlW3JvdywgZ10gPSAxDQogIH0NCn0NCg0KZm9yIChnIGluIGdlbnJlX2xpc3QpIHsNCiAgdXNlcl9wcm9maWxlW24rMSxdIDwtIHJvdW5kKGNvbE1lYW5zKHVzZXJfcHJvZmlsZSksNCkNCn0NCnVzZXJfcHJvZmlsZVtuKzEsMV0gPC0gIkFWRyINCnVzZXJfcHJvZmlsZQ0KDQpjYXQocGFzdGUwKCJVc2VyIFByb2ZpbGUgVmVjdG9ycyBhcmUgYXMgZm9sbG93czogIikpDQpjYXQocGFzdGUwKHVzZXJfcHJvZmlsZVtucm93KHVzZXJfcHJvZmlsZSksMjoyMV0pKQ0KYGBgDQoNCiMjIyBCdWlsZCBNb3ZpZSBwcm9maWxlDQpgYGB7cn0NCm1vdmllX2lkMSA8LSBtb3ZpZXNfc2VsZWN0ZWQkbW92aWVJZA0KDQpuMSA8LSBsZW5ndGgobW92aWVfaWQxKQ0KeCA8LSAwDQp4WzE6bjFdIDwtIDANCm1vdmllX3Byb2ZpbGUgPC0gZGF0YS5mcmFtZShtb3ZpZV9pZDEsQWN0aW9uPXgsIEFkdmVudHVyZT14LCBBbmltYXRpb249eCwgQ2hpbGRyZW49eCwgQ29tZWR5PXgsIENyaW1lPXgsIERvY3VtZW50YXJ5PXgsIERyYW1hPXgsIEZhbnRhc3k9eCxgRmlsbS1Ob2lyYD14LCBIb3Jyb3I9eCwgSU1BWD14LCBNdXNpY2FsPXgsIE15c3Rlcnk9eCwgUm9tYW5jZT14LCBgU2NpLUZpYD14LCBUaHJpbGxlcj14LCBXYXI9eCwgV2VzdGVybj14LGBubyBnZW5yZXMgbGlzdGVkYD14KQ0KDQpuYW1lczEgPC0gbmFtZXMobW92aWVfcHJvZmlsZSkNCmxpYnJhcnkoc3RyaW5ncikNCm5hbWVzX25ldzEgPC0gc3RyX3JlcGxhY2VfYWxsKG5hbWVzMSxjKCJGaWxtLk5vaXIiPSJGaWxtLU5vaXIiLCJTY2kuRmkiPSJTY2ktRmkiLCJuby5nZW5yZXMubGlzdGVkIj0iKG5vIGdlbnJlcyBsaXN0ZWQpIikpDQpuYW1lcyhtb3ZpZV9wcm9maWxlKSA8LSBuYW1lc19uZXcxDQoNCmdlbnJlX2xpc3QxIDwtIGMoIkFjdGlvbiIsICJBZHZlbnR1cmUiLCAiQW5pbWF0aW9uIiwgIkNoaWxkcmVuIiwgIkNvbWVkeSIsICJDcmltZSIsICJEb2N1bWVudGFyeSIsICJEcmFtYSIsICJGYW50YXN5IiwiRmlsbS1Ob2lyIiwgIkhvcnJvciIsICJJTUFYIiwgIk11c2ljYWwiLCAiTXlzdGVyeSIsICJSb21hbmNlIiwgIlNjaS1GaSIsICJUaHJpbGxlciIsICJXYXIiLCAiV2VzdGVybiIsIihubyBnZW5yZXMgbGlzdGVkKSIpDQoNCmZvciAocm93IGluIDE6bnJvdyhtb3ZpZV9wcm9maWxlKSkgew0KICBnZW5yZTEgPC0gYXMubGlzdChzdHJzcGxpdCh0b1N0cmluZyhtb3ZpZXNbbW92aWVzJG1vdmllSWQgPT0gbW92aWVfcHJvZmlsZVtyb3csIF0kbW92aWVfaWQxLCBdJGdlbnJlcyksIlt8XSIpW1sxXV0pDQogIGZvciAoZzEgaW4gZ2VucmUxKSB7DQogICAgbW92aWVfcHJvZmlsZVtyb3csIGcxXSA9IDENCiAgfQ0KfQ0KDQptb3ZpZV9wcm9maWxlDQpgYGANCg0KIyMjIENvc2luZSBTaW1pbGFyaXR5DQpgYGB7cn0NCmNvc19zaW0gPC0gTlVMTA0Kb3B0aW9ucygiZGlnaXRzIj00KQ0KDQpmb3IoaSBpbiAxOm5yb3cobW92aWVfcHJvZmlsZSkpew0KICBTaW1pbGFyaXR5IDwtIGxzYTo6Y29zaW5lKGFzLm51bWVyaWModXNlcl9wcm9maWxlW24rMSwtMV0pLCBhcy5udW1lcmljKG1vdmllX3Byb2ZpbGVbbW92aWVfcHJvZmlsZSRtb3ZpZV9pZDEgPT0gbW92aWVfcHJvZmlsZVtpLCJtb3ZpZV9pZDEiXSwtMV0pKVtbMV1dDQptb3ZpZUlkIDwtIG1vdmllX3Byb2ZpbGVbbW92aWVfcHJvZmlsZSRtb3ZpZV9pZDEgPT0gbW92aWVfcHJvZmlsZVtpLCJtb3ZpZV9pZDEiXSwxXQ0KTW92aWVOYW1lIDwtIGFzLnZlY3Rvcihtb3ZpZXNbbW92aWVzJG1vdmllSWQgPT0gbW92aWVJZCwidGl0bGUiXSlbMV0NCiNjYXQoIk1vdmllOiIsIHRpdGxlLCAiLCBhbmQgc2ltaWxhcml0eSBzY29yZToiLCBzaW1pbGFyaXR5ICwgIlxuIikNCnRtcCA8LSBkYXRhLmZyYW1lKG1vdmllSWQsTW92aWVOYW1lLFNpbWlsYXJpdHkpDQpjb3Nfc2ltIDwtIHJiaW5kKGNvc19zaW0sdG1wKQ0KfQ0KDQpwcmludChjb3Nfc2ltLCByb3cubmFtZXMgPSBGQUxTRSkNCmBgYA0KDQojIyMgUmVjb21tZW5kZWQgTW92aWVzIGZvciBVc2VyDQpgYGB7cn0NCnJlY29tbWVuZGVkIDwtIGhlYWQoY29zX3NpbVtvcmRlcigtY29zX3NpbVssM10pLF0sNSkNCg0KY2F0KHBhc3RlMCgiVXNlciBJRDogIiwgdXNlciwiIGNob3NlIHRoZSBmb2xsb3dpbmcgMTAgbW92aWVzOiIpKQ0KY2F0KHBhc3RlMCgiXG4iKSkNCnByaW50KG1vdmllX2lkMSkNCmNhdChwYXN0ZTAoIlxuIikpDQpjYXQocGFzdGUwKCJPZiB0aGVzZSwgdGhlIGZvbGxvd2luZyA1IG1vdmllcyBhcmUgcmVjb21tZW5kZWQ6IikpDQpwcmludC5kYXRhLmZyYW1lKHJlY29tbWVuZGVkLHJvdy5uYW1lcyA9IEZBTFNFKQ0KYGBgDQoNCg0K